Artificial Immune-Based For Voltage Stability Prediction In Power System S. I. Suliman, T. K. Abdul Rahman, I. Musirin Faculty of Electrical Engineering, Universiti Teknologi MARA,40450, Shah Alam, Selangor Darul Ehsan, MALAYSIA takitik@streamyx.com Abstract Voltage instability has recently become a challenging problem for many power system operators. This phenomenon has been reported to be responsible for severe low voltage condition leading to majo r blackouts. This paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary action could be taken in order to avoid the occurrence of voltage collapse. Key features of the proposed method are the implementation of clonal selection principle that has the capability in performing pattern recognition task. The proposed technique was tested on the IEEE 30 bus power system and the results shows that fast performance with accurate prediction for voltage stability condition of the system was obtained. In order to realize the superior features of AIS, a comparative study was conducted with Artificial Neural Network (ANN)-based prediction system. This system was developed to perform similar task on the same test system. Keywords: Artificial Immune Systems, Pattern Recognition, Voltage Stability. 1. Introduction Voltage stability is the ability of a power system to maintain steadily acceptable bus voltage at each node under normal operating conditions, following load increases, system configuration changes or a disturbance. The progressive and uncontrollable drop in voltage eventually results in a wide spread voltage collapse. Problems related to voltage stability issues have attracted greater concern among the power system engineers since power systems nowadays have evolved through continuing growth in interconnection and operating in highly stressed condition[1]. Therefore voltage stability evaluation becomes part of the power system operation routine and has been treated by a wide spectrum of computational approaches with major goal of determining the voltage stability condition of the system and its margin from instability condition [2]. A fast method to evaluate voltage stability condition is desirable since rapid action and accurate decision is needed so that the occurrence of voltage collapse could be avoided. Nowadays, power system control and operation has made use of intelligent systems in determining system condition for the on line monitoring system. This is due to their capability in giving fast decision or prediction with acceptable accuracy. The applications of intelligent systems have been reported to solve power system problems since early 1980’s [3]. Expert System (ES) and Artificial Neural Network (ANN) are the most common intelligent systems employed in solving various problems in power system operation and planning [3]. The learning capability of ANN has been exploited for power system security prediction as reported in references [4-7]. For example, a multi-layer feed-forward perceptron ANN was implemented for evaluating power system dynamic stability as described in reference 4. This method utilized the most critical eigenvalue obtained from the S-matrix as the indicator or index to power system dynamic stability and taken to be the output of the ANN. The relationship between the index (output) and the input quantities (nodal active and reactive power, nodal voltage magnitudes and angles) are represented as a three-layer feed-forward neural network. The neural network was constructed with the back propagation algorithm. On the hand, a supervised clustering algorithm of neural network was used to assess the dynamic stability as mentioned in reference [5]. The algorithm implemented the adaptive threshold valued to the traditional clustering method and used the supervised output as the convergence constraints. The technique has able to reduce the training time of the neural network. In reference 6, ANN was used to predict the frequency of the centre of inertia during the first ten seconds of a dynamic process after the occurrence of generator outage. The prediction from the developed ANN would help the power system operator to decide whether or not load shedding should take place using the structure of the steady-state security assessment model. AIML 06 International Conference, 13 - 15 June 2006, Sharm El Sheikh, Egypt 41