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This paper proposes an optimization classification model, which combines particle swarm optimization (PSO) with weighted knearest neighbors (WKNN), namely PWKNN. The model optimizes the weight and k parameter of WKNN to improve the detection accuracy of gearbox lubrication levels. In the experiment, the current signals of the generator are measured, and the relative frequency spectrum of the measured signals is illustrated by using fast Fourier transform (FFT). The features from the spectrum are extracted, and then the optimal weight and k parameter of WKNN are obtained by using PSO. The average detection accuracy of gearbox lubrication levels is 96% by using PWKNN, which the result shows that the proposed PWKNN can efficiently detect the lubrication level of gearboxes. The experiment also shows that the performance of the proposed PWKNN by using the current signals of the generator is superior to that by using typical vibration signals of a gearbox. In addition, the accuracy can reach 95.4% even in environments with 20 dB noise interference.

The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. The first layer involves the γ and λ, values which are introduced to describe the current condition of characteristics of the searched solution that diversifies the particles when it is converging too much on some optima. The second layer involves the partitioning of particles that tries to prevent premature convergence. With the two search mechanisms, the PSPSO presents a simpler way of making the particles communicate with each other without too much compromise of the computational time. The proposed algorithm was compared with different variants of particle swarm optimization (PSO) using benchmark functions as well as the IEEE 10-unit unit commitment problem. Results proved the effectiveness of PSPSO with different functions and proved its competitive advantage in comparison with published PSO variants. [ABSTRACT FROM AUTHOR]

This paper presents a computer technology called hybrid particle swarm optimization (HPSO) algorithm, to solve the economic dispatch (ED) problem of cogeneration systems. First outlined the status of the power system development and technical discussion, followed by test cases from the literature are used to evaluate the accuracy and validity of the algorithm. The HPSO algorithm combines genetic algorithm-specific mutation factor (MF) characteristics and the disturbance mechanism (DM), then used the exponential function to control the MF. A generating capacity adjustment mechanism (GCAM) is designed help the algorithm effectively identify the global optimum, effectively enhance the performance of the algorithm, the advantages of reducing computation time and the search range is reached. [ABSTRACT FROM AUTHOR]

Abstract: The hybrid algorithm that combined particle swarm optimization with simulated annealing behavior (SA-PSO) is proposed in this paper. The SA-PSO algorithm takes both of the advantages of good solution quality in simulated annealing and fast searching ability in particle swarm optimization. As stochastic optimization algorithms are sensitive to their parameters, proper procedure for parameters selection is introduced in this paper to improve solution quality. To verify the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimization functions with different dimensions. The comparative works have also been conducted among different algorithms under the criteria of quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the results, the SA-PSO could have higher efficiency, better quality and faster convergence speed than compared algorithms. [Copyright &y& Elsevier]

Abstract: This study aims to propose a solving approach for the thermal unit commitment (UC) problem using the mutated particle swarm optimization (MPSO) combined with a novel encoding scheme. Unlike traditional straightforward encoding arrangements, the proposed encoding method applies the load demand and spinning reserve constraints to construct a small searching space, and then put the constraints of minimum up and down-time into the encoding structure so as to shorten the searching time effectively. This novel coding scheme could effectively prevent obtaining infeasible solutions through the application of stochastic search methods, thereby dramatically improving search efficiency and solution quality. Many nonlinear characteristics of power generators, and their operational constraints, such as minimum up and down-time, spinning reserve, generation limitations, ramp rate limits, prohibited operating zones, transmission loss, and nonlinear cost functions were all considered for practical operation. The effectiveness and feasibility of the proposed approach were demonstrated by three system case studies and compared with previous literature in terms of solution quality. The simulation results reveal that the proposed approach was capable of efficiently determining higher quality solutions in resolving the thermal unit commitment problems. [ABSTRACT FROM AUTHOR]

Abstract: An artificial identification system to classify the insulation aging status of cast-resin transformer through current impulse method of partial discharge (PD) is proposed. The aging phenomenon of insulation materials belongs to a natural property and has strongly influences with the stability of power systems. Therefore, an effectively insulating identification technology plays an important role to enhance the system operating reliability. Since PD is a well known evidence of insulation degrading, a series of high voltage test with acceleration aging process to collect PD signals for identification system are conducted. Some selected statistical PD features instead of waveform are then extracted from these experimental PD signals as input data of the identification system. Also, an artificial neural network that combined particle swarm optimization is presented as the effectively identification tool. To demonstrate the effectiveness and feasibility of the proposed approach, the artificial identification system is applied on both noisy and noiseless circumstance. The experiment showed promising results with over 94% identification rate and even with 30% noise per discharge signal, an 85% successful identification rate can still be achieved. [Copyright &y& Elsevier]

Abstract: Wind, as a renewable energy source, has been generally applied as a means to reach emission reduction goals as a result of increasing concern regarding environmental protection. Although wind generation does not directly produce harmful emissions, its effect on the generation dispatch of conventional plants can actually cause an increase in emissions especially during low and medium energy demand periods. This paper presents an interactive bi-objective programming with valuable trade-off approach for solving generation dispatch which considers environmental and fuel costs inherent to large scale penetration of wind energy. An effective encoding/decoding scheme combined with simulated annealing-like particle swarm optimization is applied for the optimization methodology, thereby dramatically improving search efficiency and solution quality. The proposed approach can provide a valuable trade-off Pareto-optimal solution by following the preferences of decision makers. The non-linear characteristics of power generators and their operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones, and transmission loss are considered for practical operation. The effectiveness and feasibility of the proposed approach were demonstrated through an IEEE 30-bus test system study. The experiment showed encouraging results, suggesting that the proposed approach was capable of providing higher quality and a wider range of Pareto-optimal solutions such that the decision makers can be presented more flexible and reasonable choices. [Copyright &y& Elsevier]

An artificial recognition system of defective types for epoxy-resin transformers through acoustic emission (AE) from partial discharge (PD) experiment is proposed. PD detection is an efficient diagnosis method to prevent the failure of electric equipments arising from degrading insulation. However, most of the PD detection methods could be performed only at the shutdown period of equipments. By using AE, the online and real-time detection with defective types could be easily reached. Therefore, in this paper a series of high voltage tests were conducted on pre-faulty transformers to collect the AE signals for recognition system needed. The selected AE features instead of waveform are then extracted from these experimental AE signals for the input characteristic of recognition system. According to these features, effective identification of their defective types can be done using the proposed recognition system that combined particle swarm optimization with an artificial neural network. To demonstrate the effectiveness and feasibility of the proposed approach, the artificial recognition system is applied on both noisy and noiseless circumstances. The experiment showed encouraging results that even with 30% noise per discharge count, an 80% successful recognition rate can still be achieved. [Copyright &y& Elsevier]

The interactive bi-objective programming with valuable trade off (IBVT) approach to general capacitor placement and scheduling problems is proposed. A novel simulated annealing-like, modified particle swarm optimization (SA–PSO) is also presented and applied for better solution quality. Two main contradictory concerns, which including cost and quality properties, are considered for optimization. For practical needs, the operating and expansion constraints of distribution feeders are formulated. Also, both fixed and switched types of capacitors are included. To demonstrate the effectiveness and feasibility of the proposed method, comparative studies were conducted on an actual feeder systematically. The experiment showed encouraging results, suggesting that the proposed approach was capable of efficiently determining higher quality solutions. [Copyright &y& Elsevier]

The high penetration of distributed generation in distributed energy systems causes the variation of power loss and makes the power grid become more complicated, so this paper takes various types of optimal algorithms into account and simulates the feeder reconfiguration on the IEEE-33 system as well as the Taiwan power system. The simulation verifies linear population size reduction of successful history-based adaptive differential evolution (L-SHADE) and particle swarm optimization (PSO) fitness in different systems and provides the recommended location of distributed energy. The proposed method keeps the voltage bound of 0.95 to 1.03 p.u. of Taiwan regulation. In the IEEE-33 system, we achieved a 52.57% power loss reduction after feeder reconfiguration, and a 70.55% power loss reduction after the distributed generator was implemented and feeder reconfiguration. Under the variation of load demand and power generation of the Taiwan power system, we establish the system models by forecasting one-day load demand. Then, we propose a one-day feeder switch operation strategy by considering the switches' operation frequency with the reduction of 83.3% manual operation and recommend feeder automation to achieve feeder power loss reduction, voltage profile improvement and get regional power grid resilient configuration. [ABSTRACT FROM AUTHOR]

The electrical distribution system has experienced a number of important changes due to the integration of distributed and renewable energy resources. Optimal integration of distributed generators (DGs) and distribution network reconfiguration (DNR) of the radial network have significant impacts on the power system. The main aim of this study is to optimize the power loss reduction and DG penetration level increment while keeping the voltage profile improvements with in the permissible limit. To do so, a hybrid of analytical approach and particle swarm optimization (PSO) are proposed. The proposed approach was tested on 33-bus and 69-bus distribution networks, and significant improvements in power loss reduction, DG penetration increment, and voltage profile were achieved. Compared with the base case scenario, power loss was reduced by 89.76% and the DG penetration level was increased by 81.59% in the 69-bus test system. Similarly, a power loss reduction of 82.13% and DG penetration level increment of 80.55% was attained for the 33-bus test system. The simulation results obtained are compared with other methods published in the literature. [ABSTRACT FROM AUTHOR]

This paper investigates the feasible reserve of diesel generators in day-ahead unit commitment (DAUC) in order to handle the uncertainties of renewable energy sources. Unlike other studies that deal with the ramping of generators, this paper extends the ramp rate consideration further, using dynamic limits for the scheduling of available reserves (feasible reserve) to deal with hidden infeasible reserve issues found in the literature. The unit commitment (UC) problem is solved as a two-stage day-ahead robust scenario-based unit commitment using a metaheuristic new variant of particle swarm optimization (PSO) called partitioned step PSO (PSPSO) that can deal with the dynamic system. The PSPSO was pre-optimized and was able to find the solution for the base-case UC problem in a short time. The evaluation of the optimized UC schedules for different degrees of reserve consideration was analyzed. The results reveal that there is a significant advantage in using the feasible reserve formulation, especially for the deterministic approach, over the conventional computation in dealing with uncertainties in on-the-day operations even with the increase in the reserve schedule. [ABSTRACT FROM AUTHOR]