The performance of two stochastic search methods, particle swarm optimisation (PSO) and simulated annealing (SA), when used for fault identification of induction machine stator and rotor winding faults, is evaluated in this paper. The proposed condition monitoring technique uses time domain terminal data in conjunction with the optimization algorithm to indicate the presence of a fault and provide information about its nature and location. The technique is demonstrated using experimental data from a laboratory machine.

This paper presents an adaptive proportional resonant (PR) controller for single phase grid connected inverter that adapts its control parameters to grid impedance variations. Forth order band bass filter is designed and then integrated with the adaptive scheme for on-line detection of any variations in the resonance frequency. The estimated frequency is then processed by statistical signal processing operation to identify the variations in the grid impedance. For the on-line tuning of the PR parameters, a look-up table technique is utilized and its parameters are linked with the estimated impedance values. Simulation results based on MATLAB environment clearly verify the effectiveness of the proposed control scheme for 2 kW grid connected inverter system.

The performance of a stochastic search algorithm, Bacterial Foraging Optimization (BFO), when used for fault identification of induction machine stator and rotor winding faults, is investigated in this paper. The proposed condition monitoring technique uses time domain terminal data in conjunction with the optimization algorithm and an induction machine model to indicate the presence of a fault and provide information about its nature and location. The proposed technique is evaluated using experimental data obtained from a 1.5 kW wound rotor three-phase induction machine. BFO is shown to be effective in identifying the type and location of the fault without the need for prior knowledge of various fault signatures.

Circulating current has been an inherent feature of modular multilevel converters (MMC), which results in second-order harmonics on the arms currents. If not properly controlled, the circulating current can affect the lifetime and reliability of a converter by increasing the current loading, loss distribution, and junction temperature of its semiconductor devices. This paper proposes controlled circulating current injection as a means of improving the lifetime and reliability of an MMC. The proposed method involves modifying the reference modulating signals of the converter arms to include the controlled differential voltage as an offset. The junction temperature of the semiconductor devices obtained from an electro-thermal simulation is processed to deduce the lifetime and reliability of the converter. The obtained results are benchmarked against a case where the control method is not incorporated. The incorporation of the proposed control method results in a 68.25% increase in the expected lifetime of the converter and a 3.06% increase on its reliability index. Experimental results of a scaled down laboratory prototype validate the effectiveness of the proposed control approach.

This paper presents a novel condition monitoring scheme for sub-module (SM) capacitors in the modular multilevel converter (MMC), where the fast-affine projection (FAP) algorithm is cooperatively embedded in a dedicated range-based sorting scheme with reduced switching frequency to estimate the capacitor parameters. The proposed sorting scheme not only reduces the switching events of SMs, but also manages to maintain the capacitor ripple voltage within an acceptable range. Whilst the use of FAP achieves an on-line parametric estimation with superior speed and accuracy. Compared to previous studies, this proposed approach has significant advantages in terms of estimation accuracy and speed without the need for extra hardware. The effectiveness of the proposed approach is validated by a 5-level MMC model in MATLAB/Simulink. From simulation results, the estimation speed is less than 1.5 ms and the error is less than 3%.

This paper first outlined the motivation behind solid state transformer (SST) against conventional line frequency transformer (LFT) as well as its functional futures and benefits and secondly explore all the possible configurations of SST in terms of circuit configurations, advantages, disadvantages and their potential areas of applications. Four circuit configurations considered in this paper include; single stage SST, two stage SST with low voltage DC link (LVDC), two stage SST with high voltage DC link (HVDC) and three stage SST. Our findings reveals that apart from providing voltage regulation, SST can provide other additional ancillary services to the grid to enable it cope with transients. These services (such as power quality improvement, fault isolation, instantaneous voltage regulation, active and reactive power compensation) are not offered by LFT, as such SST is considered as the potential transformer in smart grid applications, renewable energy integration, modern traction systems and other applications where space and volume are critical.

This paper introduces a control scheme to improve efficiency as well as both the input and output current ripples of multiphase interleaved buck converter (IBC), which is suitable for applications that require a wide range of output voltage. The number of phases is dynamically changed throughout the entire range of the output voltage in order to obtain the optimum efficiency and the lowest output ripple possible. The paper demonstrates the proposed approach on a single converter of a fast DC charger module which usually consists of multiple converters connected in input-parallel output parallel (IPOP) configuration. Simulation results show that the proposed technique significantly reduces the output current ripple when compared with the conventional phaseshedding technique while maintaining satisfactory high efficiency across the entire range of the output voltage.

Under the umbrella of the Computational Intelligence (CI) the performance of a two algorithms: Particle swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO), when used for inter-turn short circuit stator winding fault of induction machine, is investigated in this paper. The proposed condition monitoring technique uses time domain terminal data in conjunction with the optimization algorithm and an induction machine model to indicate the presence of a fault and provide information about its nature and location. The proposed technique is evaluated using experimental data obtained from a 1.5 kW wound rotor three-phase induction machine. PSO and BFO are shown to be effective in identifying the type and location of the fault without the need for prior knowledge of various fault signatures.Index Terms--Induction machine, computational intelligence, condition monitoring. Measured Stator Currents Machine ModelFault Alarm Error (IAE)