C.S. Chang

An Intelligent Tuned Harmony Search algorithm for optimisation

The Harmony Search (HS) algorithm is a population-based metaheuristic optimisation algorithm. This algorithm is inspired by the music improvisation process in which the musician searches for harmony and continues to polish the pitches to obtain a better harmony. Although several variants of the HS algorithm have been proposed, their effectiveness in dealing with diverse problems is still unsatisfactory. The performances of these variants mainly depend on the selection of different parameters of the algorithm. In this paper, a new variant of the HS algorithm is proposed that maintains a proper balance between diversification and intensification throughout the search process by automatically selecting the proper pitch adjustment strategy based on its Harmony Memory. However, the performance of the proposed Intelligent Tuned Harmony Search (ITHS) algorithm is influenced by other parameters, such as the Harmony Memory Size (HMS) and the Harmony Memory Considering Rate (HMCR). The effects that varying these parameters have on the performance of the ITHS algorithm is also analysed in detail. The performance of the proposed ITHS algorithm is investigated and compared with eight state-of-the-art HS variants over 17 benchmark functions. Furthermore, to investigate the robustness of the proposed algorithm at higher dimensions, a scalability study is also performed. Finally, the numerical results obtained reflect the superiority of the proposed ITHS algorithm in terms of accuracy, convergence speed, and robustness when compared with other state-of-the-art HS variants.

Hybrid time-frequency domain analysis for inverter-fed induction motor fault detection

The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) to extract features from time domain current signals is combined with information extracted from fast Fourier transformed signal to reveal any underlying faults. To minimise the effect of the noise in the raw signal and intra-class variance in the extracted feature, a novel noise reduction approach- Ensemble and Individual Noise Reduction is employed. An advantage of the proposed scheme is that time domain analysis module can provide an early fault detection with minimal computation complexity. Experimental results obtained on the three-phase inverter-fed squirrel-cage induction motors demonstrated that the proposed method provides excellent classification results.

Optimal Thrust Allocation for Semisubmersible Oil Rig Platforms Using Improved Harmony Search Algorithm

Deep-water offshore drilling vessels, such as a semisubmersible drilling rig, use the dynamic positioning (DP) system and the thruster-assisted position-mooring system for maintaining a stationary position. In the DP system, the thrust allocator is used to distribute the desired generalized forces computed by the motion controller among the thrusters. However, to ensure safe operation of the vessel despite the thruster failure, the vessel is equipped with redundant thruster configuration and, therefore, is overactuated. For overactuated vessels, the choice of a particular solution for thrust allocation is found using some kind of optimization process. In this paper, the thrust allocator tries to minimize the power consumption and takes forbidden/spoil zones into account. The formulated thrust allocation problem becomes nonconvex due to thrust direction constraints on azimuth thrusters. The conventional methods get trapped in local minima and fail to find the optimum solution for the formulated nonconvex thrust allocation problem. In this paper, an improved harmony search (IHS) algorithm for solving the nonconvex thrust allocation problem is proposed. IHS is a variant of the harmony search (HS) algorithm. The HS algorithm is a music-based meta-heuristic optimization method, which is analogous with the music improvisation process where a musician continues to polish the pitches to obtain better harmony. Obtained numerical results show that the IHS algorithm has better convergence property when compared to the HS algorithm and the genetic algorithm (GA). Moreover, the power consumption for thrust allocation using the IHS algorithm is lower when compared with HS, GA, and Mincon (sequential quadratic programming) algorithms. The percentage savings in total power consumption for thruster allocation as compared to the Mincon algorithm for GA, HS, and IHS methods are 44.96%, 48.39%, and 51.58%, respectively.

Online fault detection of induction motors using frequency domain independent components analysis

This paper proposes an online fault detection method for induction motors using frequency-domain independent component analysis. Frequency-domain results, which are obtained by applying Fast Fourier Transform (FFT) to measured stator current time-domain waveforms, are analyzed with the aim of extracting frequency signatures of healthy and faulty motors with broken rotor-bar or bearing problem. Independent components analysis (ICA) is applied for such an aim to the FFT results. The obtained independent components as well as the FFT results are then used to obtain the combined fault signatures. The proposed method overcomes problems occurring in many existing FFT-based methods. Results using laboratory-collected data demonstrate the robustness of the proposed method, as well as its immunity against measurement noises and motor parameters.

Modeling and simulation of DC transit system with VSI-fed induction motor driven train using PSB/MATLAB

This paper presents a comprehensive simulation model of DC transit system with VSI-fed induction motor driven trains created using Power System Blockset (PSB)/MATLAB software package. The substation rectifier, the accurate track, the VSI-fed induction motor train and a detailed traction load are included in the model. The objective of this model is to study the short circuits faults that happen within the system. The model is used to simulate the train starting-up process, internal faults across DC-link capacitor and external faults across the third rail and the track. Analysis and conclusion of the simulation results are given, which shows the ineffectiveness of the conventional fault protection scheme in the DC transit system.

Improved Harmony Search algorithm based optimal design of the brushless DC wheel motor

The paper presents an optimal design method to optimize the efficiency of the brushless DC wheel motor. The optimal design of a brushless DC wheel motor is a nonlinear multimodal benchmark optimization problem. Hence, conventional methods fail to provide optimal solution. Recently developed, Harmony Search (HS) algorithm has been used for maximizing the efficiency of the brushless DC wheel motor by optimally designing the design parameters. HS algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continues to polish the pitches in order to obtain better harmony. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The numerical results obtained show that the efficiency of the brushless DC wheel motor is more for IHS algorithm when compared to HS algorithm and GA. However, the motor based on the design parameters obtained using IHS is subjected to lesser temperature rise for the same efficiency as compared to that of Ant Colony Optimization (ACO) and also IHS algorithm converges faster than other meta-heuristic approaches adopted in this paper.

Optimization of the power generation scheduling in oil-rig platforms using Genetic Algorithm

The paper focuses on the optimal scheduling of the diesel generators in the oil rig platform for the fuel optimization problem. The Specific Fuel Consumption (SFC) of the Diesel Generator is not properly modeled in the literature. An accurate model of SFC using cubic spline interpolation method is also presented. Optimization of fuel consumption at various loading conditions has been analyzed with efficient use of load scheduling of the diesel generators. The SFC model consists of discrete and non linear equations and hence conventional method fail to provide optimal solution. The Genetic Algorithm (GA) is proposed for the optimum load scheduling of diesel generators of both equal and unequal ratings. A comparison of all the methods is also presented to obtain the probable optimal solution of the problem. It is concluded that proposed SFC model is more accurate and GA gives better results than conventional methods. The proposed schemes can be used in marine power plant design and are equally applicable for any other technology.

Energy-Efficient Thrust Allocation for Semi-Submersible Oil Rig Platforms Using Improved Harmony Search Algorithm

In this paper, the thrust allocation problem for semi-submersible oil rig platform is formulated as an optimization problem, with an objective to minimize the power consumption. The electrical power consumed by the oil rig platform depends on the thrust generated by the thrusters and the efficiency of the electrical propulsion system. A detailed mathematical model to compute the efficiency of the electrical propulsion system is developed and the numerical results obtained are compared with experimental test results. The formulated energy-efficient thrust allocation problem is solved using Improved Harmony Search (IHS) algorithm. The percentage savings in total power consumption for the oil rig platform as compared to the Mincon method for Genetic Algorithm (GA), Harmony Search (HS), and IHS methods are 48.76%, 51.13%, and 53.90%, respectively. In addition, the total power consumption for energy-efficient thrust allocation approach is lesser as compared to conventional thrust allocation approach for all the considered algorithms.

Voltage sag mitigation in offshore oil rig power system by dynamic voltage restorer

Voltage sag is a common power-quality problem in offshore power systems. In contrast with land power systems, offshore oil-rig platforms are “weak grid” and “finite bus” systems characterized by limited number of diesel generators supplying power to large induction motors. Thus, voltage sag due to power supply faults or trips can severely impact their performance. Dynamic voltage restorer provides a solution to voltage sag. When voltage sag is detected, ac voltage obtained from dc power supply is injected at the point of common coupling (PCC) through a injection transformer to compensate the sag. However, during this process, undesirable harmonics is injected to the system. Given the strict regulation on power quality of offshore power systems, the total harmonic voltage distortion (THDv) needs to be within 5% at PCC. In this paper, an easy-to-implement proportional-integral (PI) controlled DVR design is proposed. In addition to instantaneous detection and accurate compensation of voltage sag, it successfully limits voltage harmonics to 0.52% by using a three-level inverter and a line-side filter capacitor. The proposed DVR design is modeled with offshore oil rig power system and simulated in the SIMULINK/MATLAB environment. Time domain simulations verify the effectiveness of the design.

Supervisory evolutionary optimization strategy for adaptive maintenance schedules

A supervisory strategy is proposed for improving the performance of an evolutionary-algorithm-based system-maintenance optimizer developed in our previous work for offshore power systems. The system-maintenance optimizer generates a set of initial maintenance plans, and exports them to an intelligent maintenance advisor connected to it for implementation. The proposed supervisory strategy uses a set of intelligent rules for adjusting the crossover and mutation rates of the present evolutionary algorithm. A mechanism is developed for refining and generalizing the supervisory rules according to the users experience. The proposed supervisory strategy aims to improve the search ability and efficiency of the present evolutionary algorithm. Merits of the proposed supervisory strategy are demonstrated in case studies using our system-maintenance optimizer.