A new approach using an artificial neural network is proposed to adapt power system stabilizer (PSS) parameters in real time. A pair of online measurements i.e., generator real-power output and power factor which are representative of the generator's operating condition, are chosen as the input signals to the neural net. The outputs of the neural net are the desired PSS parameters. The neural net, once trained by a set of input-output patterns in the training set, can yield proper PSS parameters under any generator loading condition. Digital simulations of a synchronous machine subject to a major disturbance of a three-phase fault under different operating conditions are performed to demonstrate the effectiveness of the proposed neural network. >

Tuning of power system stabilizers using an artificial neural network

This paper introduces a probabilistic framework for transient stability assessment (TSA) of power systems with high penetration of renewable generation. The critical generators and areas of the system are identified using a method based on hierarchical clustering. Furthermore, statistical analysis of several transient stability indices is performed to assess their suitability for TSA of reduced inertia systems. The proposed framework facilitates robust assessment of transient stability of uncertain power systems with reduced inertia.

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Probabilistic Framework for Transient Stability Assessment of Power Systems With High Penetration of Renewable Generation

There has been continuous development of techniques for assessing the transient stability of power systems in the uncertain environment. In this paper, a novel framework for stochastic transient stability assessment is proposed. The basic theory of stochastic calculus is first introduced to form the mathematical basis of the proposed approach. A stochastic power system model based on stochastic differential equations (SDEs) is proposed to take into account the uncertain factors such as load levels and system faults. We then present a detailed discussion on the numerical methods for solving SDEs. The stochastic Euler and Milstein schemes are introduced. The concept of strong convergence is also introduced to evaluate their accuracy. The proposed approach is tested with comprehensive case studies to validate its effectiveness.

Numerical Simulation for Stochastic Transient Stability Assessment

The paper presents a generic probabilistic framework for assessing the accuracy of online prediction of power system transient stability based on phasor measurement unit (PMU) measurements and data mining techniques. It allows fair comparison of different data mining models in terms of the accuracy of the prediction. To illustrate the concept, a decision tree (DT) method is used as an example of a data mining technique. It is implemented in a 16-machine, 68-bus test power system. Generator rotor angles and speeds provided by PMUs during post-fault condition are chosen as predictors. The performance of the DT based prediction method is tested using a wide variety of disturbances with probabilistically modeled locations, durations, types of fault and the system loading levels. The accuracy of prediction is approximately 98.5% immediately following the fault clearance and can increase to almost 100% if the prediction is made 2.5 s after the fault clearance.

Probabilistic Framework for Assessing the Accuracy of Data Mining Tool for Online Prediction of Transient Stability

A new type of power system stabiliser based on fuzzy set theory is proposed to improve the dynamic performance of a multimachine power system. To have good damping character istic over a wide range of operating conditions, speed deviation (δω) and acceleration (δω) of a machine are chosen as the input signals to the fuzzy stabiliser on that particular machine. These input signals are first characterised by a set of linguistic variables using fuzzy set notations. The fuzzy relation matrix, which gives the relationship between stabiliser inputs and stabiliser output, allows a set of fuzzy logic operations that are per formed on stabiliser inputs to obtain the desired stabiliser output. Since only local measurements are required by the fuzzy stabiliser on each generating unit, the proposed stabiliser is of decentralised output feedback form and is easy for practical implementation. The effectiveness of the proposed fuzzy stabiliser is demonstrated by a multimachine system example.

Design of fuzzy power system stabilisers for multimachine power systems

A self-tuning PID (proportional-integral-derivative) power-system stabilizer (PSS) is presented for improving the dynamic stability of a multimachine power system over a wide range of operating conditions. To maintain good damping characteristics when there is a drastic change in a system operating condition, the gain settings are adapted in real time, based on the continuously measured system inputs and outputs. The proposed self-tuning stabilizer has a decentralized structure and only local measurements within each generating unit are required for the adaptation process. The effectiveness of the proposed stabilizer is demonstrated by an example. Results indicate that, under disturbance conditions, better dynamic responses can be achieved by using the proposed self-tuning PID stabilizer than a conventional stabilizer. >

Design of self-tuning PID power system stabilizer for multimachine power systems

A transient stability study of the Taiwan power system is performed using a probabilistic approach. The stochastic nature of prefault system loading conditions, as well as other initiating factors, such as the number of faulted circuits and the location and type of faults, was recognized during the compilation of the outage statistics of the Taiwan power system. Thus, a probabilistic stability index which takes these random characteristics of system faults into account is computed by using the concept of conditional probability. In addition, the contributions of various fault events to system instability are analyzed by using Bayes' theorem. The effect of load-level uncertainties on stability indices is also examined. >

Probabilistic transient stability studies using the conditional probability approach

A novel adaptive control scheme is presented for the design of a power system stabilizer (PSS) and a static VAr (reactive volt-ampere) compensator (SVC). The developed adaptive proportional-integral (PI) controllers, whose gains are adjusted in real time using the online-measured system operating conditions and a look-up table stored in computer memory, can offer better damper effects for generator oscillations over a wide range of operating conditions than conventional PI controllers with fixed gain settings. The effects of the adaptive PSS and the adaptive SVC on generator damping and terminal voltage profile are examined by computer simulation of system dynamic responses to a major disturbance. >

Adaptive control of a synchronous machine using the auto-searching method

The dynamic stability improvement of a longitudinal power system using a power system stabilizer (PSS) and a static VAr (reactive volt-ampere) compensator (SVC) is reported. An analytical approach is developed for the determination of PSS parameters. The effect of static VAr compensators installed at several different locations along 345 kV trunk lines on system responses is examined. Results from time-domain simulations indicate that the PSS and the SVC are very effective in damping system oscillations. >

Application of power system stabilizers and static VAr compensators on a longitudinal power system

Due to fast economic growth on the island of Taiwan, load demand in Taiwan Power Company (TPC) has been rapidly increasing in the past ten years and is expected to continue throughout the next decade. By 1995, the peak load demand in Taiwan is forecasted to reach 16,000 MW, an increase of 60% from the present level of 10,000 MW. Several new generating units have been under construction to meet the future demand. As a result, additional transmission lines are needed to avoid overloads on present lines. The objective of this paper is to report the results from a recent study which was aimed at reaching an optimal transmission expansion plan by comparing the relative merits of three feasible alternatives: EHV lines (345 KV), UHV lines (765 KV), and HVDC lines (± 250 KV). At present, the main factors considered in transmission expansion planning in TPC are the maximum power transfer limits, fault currents, transient stability, system losses, and cost [1]. In addition to these factors used in current practice, oscillatory stability of the power system under each expansion plan will be extensively studied. In particular, the low frequency electromechanical oscillations which tend to appear in a longitudinal power system such as the one in Taiwan will be investigated. The dampings of these oscillation modes are essential in system operation and planning, especially in the case when the transmission line is heavily loaded.

Hsu Yuan-Yih

Papers

Design of self-tuning PID power system stabilizer for multimachine power systems

Probabilistic transient stability studies using the conditional probability approach

Adaptive control of a synchronous machine using the auto-searching method

Application of power system stabilizers and static VAr compensators on a longitudinal power system

Oscillatory stability considerations in transmission expansion planning