Optimal System Frequency Control Using Pre-Defined System Parameters Selected Using Neural Network Techniques

Abstract

The frequency of a power system provides an easy and direct method of precisely monitoring the balance between consumer load and generated demand. The control of the power generation throughout the network is a hierarchical process which requires the interaction between many layers of the command control with different amounts of interaction depending on the time scales. In many applications the speed and the accuracy of automatic digital control has replaced the much slower and less accurate manual control schemes. With the recent advent of small powerful computers, it has become possible to implement many layers of control automatically, with lime scales from several hours to a second. The automatic control activities start with a prediction of the consumer load at the central control centre and ends with closed loop controllers on the turbine generators themselves, this regulates the amount of energy supplied to the generators in response to variations in the desired and the actual values of frequency and output power. Such system frequency control is used by many utilities around the world. Many of these systems use fixed parameter control schemes which do not enable optimum control action to be undertaken under all system conditions, however, adaptive control techniques may be used to track the simevarying system and monitor the system operating point. This information may be used to provide optimum control for the turbine generators. An adaptive system frequency control scheme will use an updated system model to reduce the frequency and tie-line error to zero and such a controller may be used to minimise the variance of the system error at the future time interval. The minimum variance control scheme may be linked together with model estimation schemes to form a self-tuning regulator. However, instability problems with such controllers require extensive use of monitoring or jacketing software. The main disadvantages of such control schemes occurs when there is little information to be gained from the system under observation and the controller parameters may blow up if the system is not sufficiently exciting. To reduce the inherent problems of adaptive control schemes but to enable optimum control commands to be issued, this paper reviews the idea of matching well defined system parameters to differing system conditions. A neural network technique is used to categorize the particular system operating point and match this to the optimum control parameters. System frequency error profiles will be categorized into a number of typical profiles against which will be matched optimum system parameters. Following the recent resurgence of the non-algorithmic supervised learning, investigations were undertaken into possible applications of such techniques applied to optimal control techniques as described above. In this contribution wc will describe a learning scheme suited to the problem and implemented using a data base consisting of a set input patterns together with the corresponding targets. The objective of learning will be to extract relevant information from the data base for parameter tuning. We will also discuss the conceptual outline of a cross validation scheme by majority polling. This scheme will utilise an embedded expert system and a set of child networks generated fror.i a parent network by using genetic optimisation techniques, and trained on different subsets of the same data base. In such a scheme it will be possible to avoid noise generalisation and escape local minima. The proposed scheme will enable adaptive control techniques to function in real-time environment without the characteristic problems of an under-excited system, and/or corruption of the telemetered data, when standard optimum control systems must revert to predefined fixed parameters.