Pruning kernel regression trees for security assessment of the Crete network

This paper presents the capabilities provided by Kernel Regression Trees a hybrid non-parametric regression technique to on-line dynamic security assessment and monitoring of isolated power systems with high penetration of wind power. In the applied technique, to avoid overfitting a pruning algorithm is used to extract the security structure. This approach, which is demonstrated on the electrical power system of Crete island, proved to extract simple, interpretable, and reliable security structures. A description of the security problem and the data set generation procedure are included. Comparative results regarding performances of Regression Trees and Decision Trees are presented and discussed. INTRODUCTION In isolated power systems, like the ones operating in large islands, electric power is usually produced by Diesel units and gas turbines, resulting in high costs due to fuel imports and transportation. In these systems the production of electric energy from wind presents particular interest, especially when important wind energy potential exists, which is usual in many islands. Therefore, in these cases, significant savings of conventional fuels can be obtained by a high wind power penetration. However, it is important to ensure that the electric power system operation will not be adversely affected by an increased connection of this volatile form of energy. The main problems faced by isolated electrical power systems are related to system security, control of frequency and management of system generation reserve. A common aspect to all these problems is the requirement to ensure that sufficient reserve capacity exists within the system to compensate for sudden loss of generation. Thus, mismatches in generation and load and/or unstable system frequency control might lead to system failures. This type of instability is termed frequency instability and depends on the ability of the system to restore balance between generation and load following a severe system disturbance with minimum loss of load (Kundur et al., 1997). Generally, frequency instability problems are associated with inadequacies in equipment responses, poor coordination of control and protection equipment or insufficient generation reserve. In order to guard isolated power systems against foreseen disturbances and retain acceptable security levels, on-line dynamic security assessment functions can prove very valuable for their operation. Such functions have been developed and are integrated within an advanced control system tailored to the needs of small isolated power systems with increased wind power penetration. A pilot control system has been installed on the Greek island of Lemnos [6], an isolated Diesel-wind system with a peak load of approximately 10 MW. In this system, dynamic security assessment and monitoring are taken care of by two modules based on Decision Trees and Artificial Neural Networks. Decision Trees are used to check security for the operating schedules proposed by the economic dispatch module, with respect to characteristic wind power fluctuations. Neural Networks are used to give a real-time quantitative security evaluation of the current operating state system, by emulating the expected frequency deviation to the pre-define wind disturbance. In this way, the wind power penetration can be increased without jeopardizing the system security. To a more detailed description of the control system and the dynamic security assessment modules see (Pecas Lopes et al., 1994), (Hatziargyriou et al., 1995) and [6]. The control system developed for small isolated power systems is currently being extended within the frame of the European R&D JOULE (JOR3-CT96-0119) project to cover the needs of larger isolated systems with high wind power penetration. Larger systems are characterized by several conventional fossil-fuelled generation plants and meshed transmission networks. The dynamic behavior performance of these systems depends not only on the total load and the size of the conventional units in operation, but also on their location and the response of the available spinning reserve [6]. The objective of this paper is to present the capabilities provided by Kernel Regression Trees an hybrid non-parametric regression technique presented by Torgo in 1997 to on-line dynamic security assessment and monitoring of these systems. The security evaluation structures provided by this approach are being integrated