Multi-machine Electric Power System Research Articles

Coherence indicators of generators for express assessment of electric power system transient stability

A brief overview of the generator coherence indicators is presented, the areas and limitations of their applicability are indicated. The application of the area method for the rapid assessment of the transient stability of complex multi-machine electric power system based on the features of the dynamics of the system behavior, determined by the heterogeneous structure of the electrical network, is considered. A distinctive feature of the proposed approach is the use of the characteristics of the heterogeneity of the power system structure, which determine the presence of weak links (or cut-sets) of the electrical network, which are critical from the point of view of a possible violation of the system stability.

Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.

Об исследовании малых колебаний сложных электрических систем

The mathematical model of a controlled multimachine electric power system in matrix form is developed. The model is based on space state equations and on using the system nesting technology. By using the obtained mathematical model, it is possible to investigate small-signal stability of a controlled complex electric system through determining the eigenvalues of the system dynamics matrix. The method of decomposing the complex electric system initial model by means of semi-orthogonal matrix zero dividers is applied for shifting the system poles to the desired positions.

Transient stability enhancement of a multi-machine electric power system using an adaptive-predictive wide-area damping controller

Transient stability enhancement of a multi-machine electric power system using an adaptive-predictive wide-area damping controller

Transient stability enhancement of a multi-machine electric power system using an adaptive-predictive wide-area damping controller

This paper describes the design, development and analysis of an adaptive predictive wide-area damping controller (WADC) which is employed to enhance transient stability of a two-area power system. Based on system identification and adaptive generalised predictive control, the proposed controller is used in such a way that its output signal is added to the excitation system of the already chosen generator. A state-variable-based adaptive generalised predictive control formulation has been employed in this work. In order to increase the effectiveness of the proposed controller, a modified-regulated variable has been used which utilises the outputs at the previous instants to generate the input of predictive controller. For identifying the system model, a recursive least-squares algorithm (RLSA) has been used. The results obtained from the simulation studies clearly show the effective damping of inter-area oscillations as well as the reduction in effect of time-delay in remote signals by the proposed controller.

Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu–Beasley's Genetic Algorithm

This paper presents a Specialized Chu–Beasley's Genetic Algorithm (SCBGA) to perform coordinated tuning of the parameters of proportional-integral and supplementary damping controllers (power system stabilizers and interline power flow controller-power oscillation damping) in multi-machine electric power systems. The objective is to insert additional damping to low frequency electromechanical oscillations. The current sensitivity model was used to represent the system, therefore all of its devices and components were modeled by current injection. A novel current injection model for the interline power flow controller is presented and a static analysis is considered to validate it. The New England test system – consisting of 10 generators, 39 buses, and 46 transmission lines, divided into two areas with both local and inter-area oscillation modes – was used for the simulations. The SCBGA was compared to other five algorithms: a Random Search, a Local Search, a Simulated Annealing, a Genetic Algorithm, and a Particle Swarm Optimization method, in terms of performance for the tuning of the parameters of the controllers. The results demonstrated that the SCBGA was more efficient than these other techniques. In addition, the obtained solutions proved to be robust when variation of the loads was considered.

Power system stabilizer design based on optimal model reference adaptive system

Abstract This paper addresses an optimal model reference adaptive system (MRAS) to design power system stabilizer (PSS) in multi-machine electric power systems. Weighting factors of the proposed MRAS are adjusted by particle swarm optimization (PSO) as well as its input signal is limited by a normalization technique to assure network stability. The proposed modified-optimal MRAS-PSS is evaluated against conventional PSS to demonstrate its advantages. In order to investigate the performance of the proposed MRAS-PSS under parametric uncertainties, three operating conditions are defined and simulated. Several nonlinear and time-domain simulations are carried out to validate the viability and effectiveness of the proposed MRAS-PSS under network uncertainties.

Оценивание запасов статической устойчивости электроэнергетической системы

We propose an algebraic matrix method of reserves estimation of multimachine electric power system small signal stability (asymptotic stability) that allows for multiple calculations of stationary regimes. The method is based determinant obtained by the authors of the degeneracy conditions locally modified Jacobi matrix mathematical model of the power system. Under local changes of the matrix refers to arbitrary changes of its elements in a single row or a single column. Such changes and their location simplifies algebraic conditions singular matrix. These conditions make it possible to significantly reduce the amount of calculations produced, which in turn allows for large orders of the analyzed systems of equations a high degree of efficiency of electric power of multiple mass calculations. The paper summarizes the equivalent conditions of degeneration locally modified Jacobi matrix obtained by the technique of zero divisors.

STABILITY OF A MULTI-MACHINE POWER SYSTEM EQUIPPED WITH LQG CONTROLLERS

This paper presents the design of a novel control strategy of a Power System Stabilizer (PSS) for generators in multimachine electric power systems using the Linear Quadratic Gawssian (LQG) control technique. The controller combines both the excitation and governor control loops. The technique is applied using a sample power system which includes a number of generators connected to a large power system. An optimal full state controller has been designed and a state observer is included using Kalman Filter theory. A comprehensive stability analysis and time domain performance analysis of the system with the new controller have been presented. The results prove the robustness and powerful of proposed LQG controller in improve stability margins and adding positive with a noticeable reduction in settling time.

Multi-Machine Power System Stabilizer Design using Quantitative Feedback Theory

Power System Stabilizers (PSSs) are used to enhance damping of power system oscillations through excitation control of synchronous generator. The objective of the PSS is to generate a stabilizing signal, which produces a damping torque component on the generator shaft. Conventional PSSs are designed with the phase compensation technique in the frequency domain and include the lead-lag blocks whose parameters are determined according to a linearized power system model. The performance of Conventional PSSs (CPSSs) depends upon the generator operating point and the system parameters, but a reasonable level of robustness can be achieved depending on the tuning method. To overcome the drawbacks of CPSS, numerous techniques have been proposed in literatures. In this paper a robust method based on Quantitative Feedback Theory (QFT) Algorithm is used for tuning the PSS parameters. The proposed QFT-PSS is evaluated at a multi machine electric power system. The simulation results clearly indicate the effectiveness and validity of the proposed method.

Multi-machine power system stabilizer design by using cultural algorithms

Conventional power system stabilizer (CPSS) has been widely used as a supplementary controller to damp out the low frequency oscillations. The tuning of CPSS parameters for nonlinear power systems in a robust way in order that the overall system stability can be improved is a major drawback of CPSS. Several meta-heuristic optimization techniques have been tried out to overcome this shortcoming. This paper presents a new technique named cultural algorithms (CAs) to tune the PSS parameters. This technique is robust and computationally efficient as compared with other meta-heuristic algorithms. Simulations are carried out on two typical multi-machine electric power systems. The results clearly verify that the proposed method improves the dynamic stability of the system, especially when the operating point changes.

Robust decentralized multi-machine power system stabilizer design using quantitative feedback theory

A new robust power system stabilizer (PSS) design using Quantitative Feedback Theory (QFT) for damping electromechanical modes of oscillations and enhancing power system stability is proposed in this paper. The design procedure is carried out on a multi-input–multi-output (MIMO), non-minimum phase and unstable plant. A multi-machine electric power system with system parametric uncertainties is considered as a case study. To show the effectiveness of the QFT technique, the proposed method is compared with a conventional PSS (CPSS) whose parameters are tuned using the classical lead-lag compensation and genetic algorithms. Several nonlinear time-domain simulation tests indicate that the suggested control scheme is robust to the changes in the system parameters and also to successfully reject the disturbances. The results also show that the performance of the QFT method given in this paper is more desirable than CPSS and genetic algorithm (GA).

Dynamic stability enhancement of a multimachine electric power system using STATCOM

With the expansion of electric power systems, the size and complexity of the network is increased. One of the most important drawbacks of network expansion is the reduction in the damping torque of the whole system. The lack of damping torque can lead to fluctuations and instability in the power system. With regard to this problem, power system stabilizers (PSSs) have been widely utilized to improve power system stability. However, due to some drawbacks of the conventional PSSs, the need for finding a better substitution still remains. Therefore, in this paper, the application of a static synchronous compensator (STATCOM) to improve dynamic stability of a multimachine electric power system is presented and a supplementary stabilizer based on the STATCOM is incorporated. Intelligence optimization methods such as particle swarm optimization and genetic algorithms are considered for tuning the parameters of the proposed stabilizer. Several nonlinear time-domain simulation tests visibly show the ability of the STATCOM in damping power system oscillations.

Power system stabilizer tuning in multi machine electric power systems

Power System Stabilizers (PSS) are used to generate supplementary damping control signals for the excitation system in order to damp the Low Frequency Oscillations (LFO) of the electric power system. The PSS is usually designed based on classical control approaches but this Conventional PSS (CPSS) has some problems. The CPSS is usually designed based on a linear model of the plant for a particular operating point. However, power systems are inherently nonlinear and the operating point frequently changes. Therefore, CPSS performance may deteriorate under variations that result from nonlinear and time-variant characteristics of the controlled plant. In this paper, to develop a highperformance PSS for a wide range of operating conditions, meta-heuristic optimization methods such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are used for tuning PSS parameters. The proposed optimization methods are evaluated against each other at a multi machine electric power system considering different loading conditions. The simulation results clearly indicate the effectiveness and validity of the proposed methods.

Power system stabilizer tuning in multi machine electric power systems

Power System Stabilizers (PSS) are used to generate supplementary damping control signals for the excitation system in order to damp the Low Frequency Oscillations (LFO) of the electric power system. The PSS is usually designed based on classical control approaches but this Conventional PSS (CPSS) has some problems. The CPSS is usually designed based on a linear model of the plant for a particular operating point. However, power systems are inherently nonlinear and the operating point frequently changes. Therefore, CPSS performance may deteriorate under variations that result from nonlinear and time-variant characteristics of the controlled plant. In this paper, to develop a highperformance PSS for a wide range of operating conditions, meta-heuristic optimization methods such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are used for tuning PSS parameters. The proposed optimization methods are evaluated against each other at a multi machine electric power system considering different loading conditions. The simulation results clearly indicate the effectiveness and validity of the proposed methods.

Multi Machine Power System Identification by Using Recursive least Square Method

Electric power systems are nonlinear and complicate in nature. Along with increasing size of power systems, their complexity becomes more. Besides, by using additional and auxiliary components such as Flexible AC Transmission Systems (FACTS) devises, Power System Stabilizers (PSSs), excitation systems, turbine-governor systems and etc, the dynamic model of network is increased and power system becomes more complicate. The nonlinear dynamic model of these large power systems is easily obtained. But, the complexity makes it difficult to obtain a linear dynamic model of power system. Obtaining an appropriate linear model is necessary in order to analysis and study of power system. In tradition method, a linear dynamic model of power system is obtained by linearization of nonlinear dynamic model around an operating condition. But in large electric power systems, the linearization technique is very sophisticate and maybe impossible. Concerning this matter, in this paper a Recursive Least Square (RLS) technique is used to parameter identification in a multi machine electric power system. In this method a linear model is assumed for power system and its parameters are accurately computed by using RLS method. In order to verifying the results, the obtained linear model is compared with the nonlinear model. The simulation results show the validity of identified model, as the response of identified linear model is very near to nonlinear model.

Low Frequency Oscillation at a Multi Machine Environment by Using UPFC Tuned Based on Simulated Annealing

Unified Power Flow Controller (UPFC) is one of the most viable and important Flexible AC Transmission Systems (FACTS) devises. Application of UPFC in single machine and multi machine electric power systems has been investigated with different purposes such as power transfer capability, damping of Low Frequency Oscillations (LFO), voltage support and so forth. But, an important issue in UPFC applications is to find optimal parameters of UPFC controllers. This paper presents the application of Unified Power Flow Controller (UPFC) to enhance dynamic stability of a multi-machine electric power system. A supplementary stabilizer based on UPFC (like power system stabilizer) is designed to reach the defined purpose. An intelligence optimization method based on Simulated Annealing (SA) is considered for tuning the parameters of UPFC supplementary stabilizer. Several nonlinear time-domain simulation tests visibly show the ability of UPFC in damping of power system oscillations and consequently stability enhancement

Dynamic stability enhancement and voltage support using UPFC tuned genetic algorithms in a multi- machine environment

Unified power flow controller (UPFC) is one of the most important flexible AC transmission systems (FACTS) devices in power systems. It has many applications such as stability enhancement, power flow control, voltage support, etc. In this paper UPFC is used in order to control voltage. In this scope, UPFC is installed on a specified bus and the voltage of the proposed bus is controlled by using UPFC. In order to optimize performance, the parameters of UPFC controllers are tuned using genetic algorithms (GA) optimization method. Simulation results, which are carried out on a multi-machine electric power system, clearly show effectiveness and viability of UPFC in voltage control. Key words: Unified power flow controller, voltage Support, dynamic stability enhancement, multi-machine electric power system genetic algorithms.

Voltage support and stability improvement using unified power flow controller tuned based on simulated annealing

In this paper, the authors are willing to study the effects of unified power flow controller (UPFC) on voltage support and also stability enhancement at a multi-machine electric power system. Proportional-Integral (PI) type controllers are considered for UPFC control and the parameters of these PI type controllers are tuned using simulated annealing (SA). In order to show the ability of UPFC in voltage control and also stability enhancement, the results of the system with UPFC are compared with the results without UPFC. Nonlinear time domain simulation results emphases on the ability of UPFC in simultaneous control of voltage and also stability enhancement Key words: Unified power flow controller, voltage support, dynamic stability enhancement,multi-machine electric power system, simulated annealing.

Harmony search algorithm for power system stabilizer tuning

The problem of tuning Power system stabilizers (PSSs) is commonly formulated as a nonlinear optimization problem with some constraints. In this paper a new optimization technique based on Harmony Search (HS) to solve the proposed optimization problem is presented. This is a very complex nonlinear programming problem. A constructive heuristic algorithm aimed at obtaining an excellent quality solution for this problem is presented. Results of the tests, carried out with a multi machine electric power system, show the capabilities of the method and also the viability of using the HS to solve the problem.