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The paper presents a coordinated artificial neural network (ANN) based proportional plus-integral (PI) controller for a superconducting generator (SCG). The ANN controller was trained using data groups, that covers the whole P-Q plane. These data groups obtained from a PI controller implemented on the governor control loop. To test the present control strategy, a fairly detailed nonlinear model of the SCG is used to assure accuracy and validity of the proposed control structure. The simulation results are presented in comparison with similar results which obtained using the conventional PI controller. The simulation results reveal that the ANN control design approach can be implemented to design controllers for the SCG and give results similar to those of conventional controllers. The natural growth of population renders continuous increase in electric power demand. One way of overcoming this problem is to develop SCGs. This machine offers a lot of advantages over the conventional synchronous machines and has the capability to supply greater base load with higher efficiency [1,2]. However, SCGs have a complex structure and require materials different fiom those normally used in conventional generators. This is due to that the high current density of the superconducting field winding obviates the need for any magnetic circuit in the SCG. For this reason all armature windings contemplated for SCG are of the air cored winding type. Also, the rotor incorporates in addition to the superconducting field winding, a helium management system and two eddy-current screens [3,4]. The outer screen acts as a damper has time constant suitable for the damping purpose and the inner screen acts as an electromagnetic shield has a time constant sufficiently long to shield the field winding fiom time changing magnetic fields [5]. Manuscript received from Dr .G.A. Morsy Accepted on : 17 1 6 1 2001 Engineering Research Journal Vol 24,No 3, 2001 Minufiya University, Faculty Of Engineering , Shebien El-Kom , Egypt , ISSN 1110-1 180 From the control stand point, the SCG requires special attention and considerations. This is due to that SCG has almost a zero resistance field winding and consequently an extremely long of the field circuit time constant, it has been indicated that excitation control becomes ineffective [6,7]. So, it is necessary to consider only the governor control loop to enhance the system performance. Various types of controllers such as phase-advance and proportional plus-integral have been employed. The parameters of these controllers are normally fixed at certain values which are determined for a particular operating conditions. In daily operation of power system the operating conditions changes as a result of load changes or other disturbances. Thus, a set of controller parameters which insure good performance under a certain operating conditions may no longer yield satisfactory results for others. To overcome this problems, self tuning controllers were adapted and developed to maintain better performance under a wide range of operating conditions [8]. This controller was designed and implemented on the governor loop of the SCG and successfid simulation results were obtained. However, the self tuning controllers suffers from the necessity of obtaining a suitable identification algorithm to estimate the model parameters every sampling interval and this may face practical problems. In recent years, an ANNs have attracted considerable attention as candidates for novel computational systems because of the variety of advantages that they offer over the conventional computational systems. Among those advantages, the ability to memorize, rapidity and robustness are the most profound and interesting properties which have attracted attention in many fields. Since 1989, ANN methodology has captured the interest in electrical power engineering and it is promise, if it is properly trained, to ensure effective damping action in the face of large disturbances. This is due to the adaptive nature of the ANN and thus it will overcome the problem of tuning the parameters as the power system configuration change. Many published papers were developed ANN schemes for the conventional synchronous generators which trained using power system stabilizer [9-111. As a step forward in the power system control area, this paper introduces the ANN control strategy as an alternative approach to improve the low inherent damping characteristics of the SCGs. The ANN scheme was trained using data groups obtained from the conventional PI control on the governor loop at the SCG. The proposed structure of the ANN controller has an input layer, an output layer and one hidden layer. The simulation results obtained using a detailed nonlinear model and pressed in comparison with those obtained with conventional PI controller emphasizing robustness and flexibility of the proposed ANN controller. 2. POWER SYSTEM MODEL The power system model used in this study is shown in Fig.(l). It consists of a SCG connected to an infinite bus via a double circuit transmission line and a transformer. The generator is driven by a three-stage steam turbine with reheat and fast acting electro-hydraulic governor. A selection switch has been used to consider the control action. Exciter Vt & + Governor AVR Vb

AN ANN BASED PI CONTROLLER FOR A SUPERCONDUCTING GENERATOR