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Energized parts within power transformer are isolated using paper insulation and are immersed in insulating oil. Hence, transformer oil and paper insulation are essential sources to detect incipient and fast developing power transformer faults. Several chemical diagnoses techniques are developed to examine the condition of paper insulation such as degree of polymerization, carbon oxides, furanic compounds and methanol. The principle and limitation of these diagnoses are discussed and compared in this paper.

Doubly Fed Induction Generators (DFIGs) are widely used in variable speed wind turbine owing to its superior advantages that include ability to extract more energy from turbine, capability to control active and reactive power independently and the usage of reduced converter rating that reduces its overall cost. The application of DFIG in large wind energy conversion systems (WECS) has reached 55% of the worldwide total wind capacity during the year 2012. On the other side fluctuating output power, weak fault ride through capability and high sensitivity to grid disturbances are the main issues that affect DFIG performance. In this paper, superconducting magnetic energy storage (SMES) unit is proposed to improve the overall performance of a DFIG-based WECS during voltage sag disturbance in the grid side. A new control approach for SMES unit using hysteresis current controller (HCC) along with proportional integral (PI) controller is introduced. Simulations results reveal the effectiveness of the proposed SMES controller in improving the overall performance of the WECS system under study.

On load tap changing (OLTC) transformer has become a vital link in modern power systems. It acts to maintain the load bus voltage within its permissible limits despite any load changes. This paper discusses the effect of different static loads namely; constant power (CP), constant current (CI) and constant impedance (CZ) on the maximum power transfer limit from the generation to the load centre through the OLTC branch and in turn on the static stability margin of power systems. Then the paper introduces a novel approach for the on-line determination of the OLTC settings using artificial neural network (ANN) technique in order to improve the power transfer capability of transmission systems. The proposed approach is tested on a sixbus IEEE system. Numerical results show that the setting of OLTC transformer in terms of the load model has a major effect on the maximum power transfer in power systems and the proposed ANN technique is very accurate and reliable. The adaptive settings of OLTC improve the power transfer capability according to the system operating condition.

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