Wu Chi-Jui

Bio: Wu Chi-Jui is an academic researcher from National Taiwan University. The author has contributed to research in topics: Electric power system & Static VAR compensator. The author has an hindex of 2, co-authored 2 publications receiving 116 citations.

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

Abstract: 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. >

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

Abstract: 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. >

Tuning of power system stabilizers using an artificial neural network

Abstract: 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. >

Design of fuzzy power system stabilisers for multimachine power systems

Abstract: 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.

An adaptive control scheme for speed control of diesel driven power-plants

Abstract: The performance of an incremental Clarke-Gawthrop adaptive control scheme, suitable for a diesel-engine prime mover, is described. The controller uses a predictor that is derived from explicit estimates of the plant deadtime and time constants. Its performance under speed reference changes and load disturbances has been compared to that of a fixed, tuned proportional-integral (PI) controller. The algorithm is found to operate satisfactorily under different values of droop without any additional complexity of computation being incurred. However, the improvement in plant response due to the adaptive algorithm is somewhat reduced at high droops. The effective improvement due to adaptation is also seen to be reduced under 'cold oil' conditions. However, even under such conditions, it is possible to obtain improved response as compared to the PI controller. >

A hybrid neuro-fuzzy power system stabilizer for multimachine power systems

Abstract: A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters in real-time based on machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN model. Time domain simulations of a single machine infinite bus system and a multimachine power system subject to major disturbances are investigated. The performance of the proposed FBFN PSS is compared with that of conventional (CPSS). The results show the capability of the proposed FBFN PSS to enhance the system damping of local modes of oscillations over a wide range of operating conditions. The decentralized nature of the proposed FBFN PSS makes it easy to install and tune.