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This paper presents the dynamic operation and control strategies of a hybrid wind-diesel-battery energy storage based power supply system for isolated communities are investigated. Control strategies for voltage and frequency stabilization and efficient power flow among the hybrid system components are developed. The voltage and frequency of the hybrid wind-diesel system is controlled either by a load side inverter or by diesel generation depending on the wind conditions. During high penetration of wind, the wind turbine supplies the required power to the load. A battery energy storage system is connected to the dc-link to balance the power generated from the wind turbine and the power demand by load. Under low wind conditions, a diesel generator is used with wind energy conversion system to generate the required power to the load. A power sharing technique is developed to allocate power generation for diesel generator in low wind conditions. Results show that the control strategies work very well under dynamic and steady state condition to supply power to the load.Index Terms-diesel generator; dump load; droop mode; isochonous mode; permanent magnet synchronous generator; power electronic converters; wind-diesel hybrid power system; wind energy conversion system.

The growing electricity demand and scarcity of energy sources are the driving force of exploration of the renewable energy sources. However, uncertain energy production of these sources makes difficulties in their control and operation with or without a grid utility connection. This paper proposes a novel power control strategy which employs a voltage band in the DC-link voltage to maintain voltage stability and thereby improve the power quality during temporary disturbances in a network. It is applied to act as an inverter's inertia and obtain maximum benefit from the storage system, based on the features of a DC busbar to avoid a voltage limit violation. It also presents a demand dispatch strategy based on a client's terminal voltage, which avoids the complexity of communication lines, for balancing the network power during power shortages. The developed control algorithm is tested on an IEEE 16 bus test feeder with multiple DG units and from the results it is found that the proposed control methods provide effective control of the voltage and power quality during transient conditions and scarce power generation.

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The control scheme of an arc furnace electrode positioning system aims to deliver an optimum stable reaction zone below the electrodes by maintaining a fixpoint resistance. However, because of random movement of melted materials during melting period, the resistance of the arc furnace changes randomly. As a result, the electrodes have to move accordingly to obtain a fix-point resistance. Moreover, it is often found that the arc furnace resistance changes very fast and it is impossible for the electrode to track the random change of resistance. Consequently, the furnace becomes unstable and it is often impossible to achieve required production per unit power. Hence, the control system often relies on prediction tools. However, it is difficult to predict the arc furnace resistance using conventional mathematical models. As a result, in this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to capture the random and time-varying nature of arc furnace resistance. The performance of the proposed model is evaluated by presenting a case study where the outputs of the proposed model are compared with the data recorded from an actual metallurgical plant.

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