A new analytic approach for power system fault diagnosis employing the temporal information of alarm messages

Traditional analytic models for power system fault diagnosis are usually formulated as an unconstrained 0–1 integer programming problem. The key issue of the models is to seek the fault hypothesis that minimizes the discrepancy between the actual and the expected states of the concerned protective relays and circuit breakers. The temporal information of alarm messages has not been well utilized in these methods, and as a result, the diagnosis results may be not unique and hence indefinite, especially when complicated and multiple faults occur. In order to solve this problem, this paper presents a novel analytic model employing the temporal information of alarm messages along with the concept of related path. The temporal relationship among the actions of protective relays and circuit breakers, and the different protection configurations in a modern power system can be reasonably represented by the developed model, and therefore, the diagnosed results will be more definite under different circumstances of faults. Finally, an actual power system fault was served to verify the proposed method.

A new approach for power system black-start decision-making with vague set theory

Power system restoration after a large area outage involves many factors, and the procedure is usually very complicated. A decision-making support system could then be developed so as to find the optimal black-start strategy. In order to evaluate candidate black-start strategies, some indices, usually both qualitative and quantitative, are employed. However, it may not be possible to directly synthesize these indices, and different extents of interactions may exist among these indices. In the existing black-start decision-making methods, qualitative and quantitative indices cannot be well synthesized, and the interactions among different indices are not taken into account. The vague set, an extended version of the well-developed fuzzy set, could be employed to deal with decision-making problems with interacting attributes. Given this background, the vague set is first employed in this work to represent the indices for facilitating the comparisons among them. Then, a concept of the vague-valued fuzzy measure is presented, and on that basis a mathematical model for black-start decision-making developed. Compared with the existing methods, the proposed method could deal with the interactions among indices and more reasonably represent the fuzzy information. Finally, an actual power system is served for demonstrating the basic features of the developed model and method.

Power system stability enhancement in a deregulated environment using FACTS devices

In deregulated versions of free-market electricity, producers will be free to send power along other utilities. The price of power strongly depends and fluctuates according to mutual benefit index of both supplier and consumer. In such a situation, strong interaction among utilities may cause instabilities in the system. As the frequency of market-based dispatch increases market forces tend to destabilize the stable system dynamics depending on the value of Ks/τλ(market dependent parameter) ratio. This tends to destabilize the coupled dynamics. The implementation of TCSC can effectively damp the inter area modes of oscillations of the coupled market system.

Investigation of sensitivity based load ranking with composite load model in power system

Load modeling plays an important role in power system dynamic stability assessment. One of the widely used methods in assessing load model impact on system dynamic response is through parametric sensitivity analysis. Load ranking provides an effective measure of such impact. Traditionally, load ranking is based on either static or dynamic load model alone. In this paper, composite load model based load ranking framework is proposed. It enables comprehensive investigation into load modeling impacts on system stability considering the dynamic interactions between load and system dynamics. The impact of load composition on the overall sensitivity and therefore on ranking of the load is also investigated. Dynamic simulations are performed to further elucidate the results obtained through sensitivity based load ranking approach.

Power system sensitivity analysis considering induction motor loads

Power system operation and planning are facing increasing uncertainties especially with the deregulation process and increasing demand for power. Probabilistic power system stability assessment and probabilistic power system planning have been identified by EPRI as one of the important trends in power system operations and planning. Probabilistic small signal stability assessment studies the impact of system parameter uncertainties on system small disturbance stability characteristics. Researches in this area have covered many uncertainties factors such as controller parameter uncertainties and generation uncertainties. One of the most important factors in power system stability assessment is load dynamics. In this paper, composite load model is used to consider the uncertainties from load parameter uncertainties impact on system small signal stability characteristics. The results provide useful insight into the significant stability impact brought to the system by load dynamics. They can be used to help system operators in system operation and planning analysis.

Performance comparison of four power system stabilizer

The well-known power system stabilizer (PSS) is used to generate supplementary control signals for the excitation system of a generator so as to damp low frequency oscillations in the power system concerned. Up to now, various kinds of PSS design methods have been proposed and some of them applied in actual power systems with different degrees. Given this background, the small-disturbance eigenvalue analysis and large-disturbance dynamic simulations in the time domain are carried out to evaluate the performances of four different PSS design methods, including the Conventional PSS (CPSS), Single-Neuron PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). To make the comparisons equitable, the parameters of the four kinds of PSSs are all determined by the steepest descent method. Finally, an 8-unit 24-bus power system is employed to demonstrate the performances of the four kinds of PSSs by the well-established eigenvalue analysis as well as numerous digital simulations, and some useful conclusions obtained.

Small signal stability analysis of power systems with high penetration of wind power

The integration of large amount of wind power into a power system imposes a new challenge for the secure and economic operation of the system. It is necessary to investigate the impacts of wind power generation on the dynamic behavior of the power system concerned. This paper investigates the impacts of large amount of wind power on small signal stability and the corresponding control strategies to mitigate the negative effects. The concepts of different types of wind turbine generators (WTGs) and the principles of the grid-connected structures of wind power generation systems are first briefly introduced. Then, the state-of-the-art of the studies on the impacts of WTGs on small signal stability as well as potential problems to be studied are clarified. Finally, the control strategies on WTGs to enhance power system damping characteristics are presented.

Impacts of different wind power generators on power system small signal and transient stability

With the ever-increasing penetration level of wind power, the impacts of wind power on the power system are becoming more and more significant. Hence, it is necessary to systematically examine its impacts on the small signal stability and transient stability in order to find out countermeasures. As such, a comprehensive study is carried out to compare the dynamic performances of power system respectively with three widely-used power generators. First, the dynamic models are described for three types of wind power generators, i. e. the squirrel cage induction generator (SCIG), doubly fed induction generator (DFIG) and permanent magnet generator (PMG). Then, the impacts of these wind power generators on the small signal stability and transient stability are compared with that of a substituted synchronous generator (SG) in the WSCC three-machine nine-bus system by the eigenvalue analysis and dynamic time-domain simulations. Simulation results show that the impacts of different wind power generators are different under small and large disturbances.

Investigation of the effects of various types of wind turbine generators on power-system stability

It has become more and more demanding to investigate the impacts of wind farms on power system operation as ever-increasing penetration levels of wind power have the potential to bring about a series of dynamic stability problems for power systems. This paper undertakes such an investigation through investigating the small signal and transient stabilities of power systems that are separately integrated with three types of wind turbine generators (WTGs), namely the squirrel cage induction generator (SCIG), the doubly fed induction generator (DFIG), and the permanent magnet generator (PMG). To examine the effects of these WTGs on a power system with regard to its stability under different operating conditions, a selected synchronous generator (SG) of the well-known Western Electricity Coordinating Council (WECC three-unit nine-bus system and an eight-unit 24-bus system is replaced in turn by each type of WTG with the same capacity. The performances of the power system in response to the disturbances are then systematically compared. Specifically, the following comparisons are undertaken: (1) performances of the power system before and after the integration of the WTGs; and (2) performances of the power system and the associated consequences when the SCIG, DFIG, or PMG are separately connected to the system. These stability case studies utilize both eigenvalue analysis and dynamic time-domain simulation methods.

Identification of reduced models of power system areas using phasor measurement units

In this paper an approach is presented for identification of a reduced model for coherent areas in power systems using phasor measurement units to represent the inter-area oscillations of the system. The generators which are coherent in a wide range of operating conditions form the areas in power systems and the reduced model is obtained by representing each area by an equivalent machine. The reduced nonlinear model is then identified based on the data obtained from measurement units. The simulation is performed on three test systems and the obtained results show high accuracy of identification process.

Probabilistic power system contingency analysis considering wind

This thesis was a step forward in developing probabilistic assessment of power system response to faults subject to intermittent generation by renewable energy. It has investigated the wind power fluctuation effect on power system stability, and the developed fast estimation process has demonstrated the feasibility for real-time implementation. A better balance between power network security and efficiency can be achieved based on this research outcome.

Battery energy storage systems to improve power system frequency response

Battery energy storage systems (BESS) are becoming feasible to provide system frequency support due to recent developments in technologies and plummeting cost. Adequate response of these devices becomes critical as the penetration of the renewable energy sources increases in the power system. This paper proposes effective use of BESS to improve system frequency performance. The optimal capacity and the operation scheme of BESS for frequency regulation are obtained using two staged optimization process. Furthermore, the effectiveness of BESS for improving the system frequency response is verified using dynamic simulations.

A Fixed Order Model Matching Approach for the Design of Robust Power System Oscillation Damper

This book focuses on how evolutionary computing techniques benefit engineering research and development tasks by converting practical problems of growing complexities into simple formulations, thus largely reducing development efforts. This book begins with an overview of the optimization theory and modern evolutionary computing techniques, and goes on to cover specific applications of evolutionary computing to power system optimization and control problems.

Rough Fuzzy Control of SVC for Power System Stability Enhancement

Modern power systems have become more complex due to the growth in load demand, the installation of Flexible AC Transmission Systems (FACTS) devices and the integration of new HVDC links into existing AC grids. On the other hand, the introduction of the deregulated and unbundled power market operational mechanism, together with present changes in generation sources including connections of large renewable energy generation with intermittent feature in nature, have further increased the complexity and uncertainty for power system operation and control. System operators and engineers have to confront a series of technical challenges from the operation of currently interconnected power systems. Among the many challenges, how to evaluate the steady state and dynamic behaviors of existing interconnected power systems effectively and accurately using more powerful computational analysis models and approaches becomes one of the key issues in power engineering. The traditional computing techniques have been widely used in various fields for power system analysis with varying degrees of success. The rapid development of computational intelligence, such as neural networks, fuzzy systems and evolutionary computation, provides tools and opportunities to solve the complex technical problems in power system planning, operation and control.