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Flux-switching permanent magnet (FSPM) machines have attracted wide attention in many rotating applications that require high-power density. In this research, we propose for the first time a novel six-phase FSPM generator with a stator featuring a V-shaped flux-focusing magnet arrangement. The design is targeted for low-speed wind power generation. To achieve the design objectives as a wind generator, the highly comprehensive structural parameters, including the number of rotor poles, split ratio, and rotor pole width, are designed and optimized using 2D finite-element analysis. From findings, the optimal stator/rotor pole combination is discovered to be 12/19 for the considered power and speed requirements. When compared to the initial structure, the optimized structure of the V-shaped FSPM generator is found to produce a significant improvement in EMF, cogging torque, electromagnetic torque, power, and efficiency. The power-generating performance of the proposed FSPM generator is found to be outstanding when compared to the radial-flux PM generators described in the literature. Therefore, the proposed V-shaped FSPM generator is capable of being used for low-speed wind power generation. The machine configuration adjustment approach presented in this work can also be utilized for the design of permanent magnet wind generators. [ABSTRACT FROM AUTHOR]

The rotor permanent magnet flux-switching (RPM-FS) machine is a promising candidate for electric vehicle (EV) and hybrid electric vehicle (HEV) applications. In this paper, we propose the magnetic flux barrier design to improve the torque capability of the RPM-FS machine. The response surface optimization method was used to design and optimize the topology of flux barriers. The 2D finite element analysis shows that the proposed RPM-FS machine has a higher electromotive force than the conventional structure, with only a slight increase in cogging torque. Notably, an insertion of flux barriers could yield a reduction of magnetic flux leakage, an improvement of magnetic saturation capability, and an enhancement of working harmonics of the air-gap flux density. As a result, a significant improvement in torque capability, eddy current losses, and efficiency was obtained. Hence, the RPM-FS machine proposed in this work is capable of being used in EV and HEV applications. [ABSTRACT FROM AUTHOR]

The complexity and uncertainty of power sources connected to transmission networks need to be considered. Planners need information on the sustainability and economics of transmission network expansion planning (TNEP). This work presents a newly proposed method for TNEP that considers high-penetration solar energy by using the particle swarm optimization (PSO) algorithm. The power sources, thermal and hydropower plants, and conditions of load were set in the account, including an uncertain power source and solar energy (PV). The optimal sizing and locating of the PV to be connected to the network were determined by the PSO. The PV grid code was set in the account. The new line's investment cost and equipment was analyzed. The PV cost was considered based on the power loss, and the system's reliability was improved. The IEEE 118 bus test system and Lao PDR's system were requested to test the proposed practice. The results demonstrate that the proposed TNEP method is robust and feasible. The simulation results will be applied to guide the power system planning of Lao PDR. [ABSTRACT FROM AUTHOR]

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called "adaptive ABC-PSO" as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system's performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system. [ABSTRACT FROM AUTHOR]

The optimal siting and sizing of battery energy storage system (BESS) is proposed in this study to improve the performance of the seventh feeder at Nakhon Phanom substation, which is a distribution network with the connected photovoltaic (PV) in Thailand. The considered objective function aims to improve the distribution network performance by minimizing costs incurred in the distribution network within a day, comprising of voltage regulation cost, real power loss cost, and peak demand cost. Particle swarm optimization (PSO) is applied to solve the optimization problem. It is found that the optimal siting and sizing of the BESS installation could improve the performance of the distribution network in terms of cost minimization, voltage profile, real power loss, and peak demand. The results are investigated from three cases where case 1 is without PV and BESS installation, case 2 is with only PV installation, and case 3 is with PV and BESS installations. The comparison results show that case 3 provided the best costs, voltage deviation, real power loss, and peak demand compared to those of cases 1 and 2; system costs provided by cases 1, 2 and 3 are USD 4598, USD 5418, and USD 1467, respectively. [ABSTRACT FROM AUTHOR]

The doubly salient permanent magnet (DSPM) machines are very attractive for low-speed power generation. In this work, we propose a design technique to improve the output power of the DSPM generator by an adjustment of pole configuration. The number of stator and rotor poles, split ratio, as well as the stator pole arc of the generator, were proposedly adjusted and optimized. The output characteristics of the generator including the magnetic flux linkage, electromotive force, harmonic, cogging torque, electromagnetic torque, output voltage and output power were analyzed through finite element analysis. The symmetrical magnetic field distribution of all generators was firstly verified. Then, the results indicated that this particular generator was optimized at 18 stator poles and 12 rotor poles, while the split ratio and the stator pole arc should be set as 0.78 and 6.15 degrees, respectively. The proposed optimal generator could provide a significant improvement in the output voltage and the output power compared to the conventional structure. The output power of 1.28 kW can be reached by the optimal structure, which was two times higher than that of the conventional structure. The physical explanation regarding to the structural modification was also given. The proposed design technique can be applied for improving the output power of the DSPM machines. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) replacing the internal combustion engine vehicle may be the solution for the particulate matter (PM) 2.5 pollution issue. However, the uncontrolled charging of EVs would challenge the power system operation. Therefore, it is necessary to implement some level of control over the EV charging procedure, especially in the residential network. In this paper, an optimization of EVs charging scheduling considering energy arbitrage and the distribution network cost of an urban village environment is presented. The optimized strategy focuses on decreasing the loss of EV owners' energy arbitrage benefit, introduced as the penalty cost. Also, peak demand, power loss, and transformer aging are included in the estimation of the cost function for the distribution network. The optimization problem is solved using the genetic algorithm. As a case study, data from the urban village in Udon Thani, Thailand, are utilized to demonstrate the applicability of the proposed method. Simulation results show a reduction in the loss of energy arbitrage benefit, transformer peak load, power loss and the transformer loss of life. Therefore, the application of the optimized EV charging can prolong transformer lifetime benefiting both the EV owner and the distribution system operator. [ABSTRACT FROM AUTHOR]

This work aims to maximize the benefit of the low-voltage (LV) level distribution system with high photovoltaic (PV) penetration by using an optimal installation of a battery energy storage system (BESS) and capacitor. The 41-bus practical distribution system located in Thailand was focused on. The comprehensive objective function regarding the focused system was proposed. The Salp Swarm and Genetic Algorithms were applied to solve the optimization problem. The total net present value (NPV) of utility was performed as a beneficial indicator, and it was determined by the overall costs and benefits of BESS installation and capacitor placement. A comparison of total NPV in the cases of centralized BESS installation, BESS installation with LV capacitor placement, and decentralized BESS installation was indicated. The results showed that all cases of BESS installation could increasingly flatten the load on the transformer; meanwhile, the voltage profile of the system was significantly improved. Optimal installation of centralized BESS simultaneously with LV capacitor placement provides higher NPV than the case with only centralized BESS installation. In particular, the highest NPV was obtained in the case of installing decentralized BESS. The results can be utilized to maximize the benefits of the utility in the distribution system at a high PV penetration level. [ABSTRACT FROM AUTHOR]

An optimal stator design technique of a three-phase doubly salient permanent magnet generator (DSPMG) for improving the soutput power is proposed. The stator configuration was optimally designed by adjusting the stator pole depth and stator pole arc. The trapezoid outer stator tip was also designed. Then, the output characteristics of the designed DSPMG including the flux linkage, electromotive force (EMF), harmonic, cogging torque, efficiency, magnetic flux distribution and voltage regulation were characterized by using the finite element method. Results were compared to the original structure in the literature. It was found that the flux linkage, EMF, cogging torque, and efficiency of the proposed DSPMG were significantly improved after the stator pole depth and stator pole arc were suitably modified. Further details of optimal stator pole depth and stator pole arc are presented. The EMF produced by the optimal proposed structure was 47% higher than that of the conventional structure, while 56% cogging torque improvement and 20% increased efficiency were achieved. The EMF generated by the proposed structure was classified in the high-range scale compared to the other existing models. The symmetrical magnetic flux distribution of all structures was indicated. The voltage regulation of the modified structure was also significantly improved from the conventional model. The proposed design technique can be utilized to maximize the electromagnetic performance of this particular generator type. [ABSTRACT FROM AUTHOR]

Solving the Unit Commitment problem is an important step in optimally dispatching the available generation and involves two stages—deciding which generators to commit, and then deciding their power output (economic dispatch). The Unit Commitment problem is a mixed-integer combinational optimization problem that traditional optimization techniques struggle to solve, and metaheuristic techniques are better suited. Dragonfly algorithm (DA) and particle swarm optimization (PSO) are two such metaheuristic techniques, and recently a hybrid (DA-PSO), to make use of the best features of both, has been proposed. The original DA-PSO optimization is unable to solve the Unit Commitment problem because this is a mixed-integer optimization problem. However, this paper proposes a new and improved DA-PSO optimization (referred to as iDA-PSO) for solving the unit commitment and economic dispatch problems. The iDA-PSO employs a sigmoid function to find the optimal on/off status of units, which is the mixed-integer part of obtaining the Unit Commitment problem. To verify the effectiveness of the iDA-PSO approach, it was tested on four different-sized systems (5-unit, 6-unit, 10-unit, and 26-unit systems). The unit commitment, generation schedule, total generation cost, and time were compared with those obtained by other algorithms in the literature. The simulation results show iDA-PSO is a promising technique and is superior to many other algorithms in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive optimization algorithm to solve the network reconfiguration and distributed generation (DG) placement problems with objective functions including power loss minimization and voltage stability index (VSI) improvement. The proposed technique called Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) was performed for solving network reconfiguration and DG installation in IEEE 33- and 69-bus distribution systems with seven different scenarios. The performance of ASFLA was compared to that of other algorithms such as Fireworks Algorithm (FWA), Adaptive Cuckoo Search Algorithm (ACSA) and Shuffled Frogs Leaping Algorithm (SFLA). It was found that the power loss and VSI provided by ASFLA were better than those given by FWA, ACSA and SFLA in both 33- and 69-bus systems. The best solution of power loss reduction and VSI improvement of both 33- and 69-bus systems was achieved when the network reconfiguration with optimal sizing and the location DG were simultaneously implemented. From our analysis, it was indicated that the ASFLA could provide better solutions than other methods since the generating process, local and global searching of this algorithm were significantly improved from a conventional method. Hence, the ASFLA becomes another effective algorithm for solving network reconfiguration and DG placement problems in electrical distribution systems. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized solutions for the power system. The hybrid algorithm adopts the exploration and exploitation phases of the DA and PSO algorithms, respectively, and was implemented to solve the MO-OPF problem. The objective functions of the OPF were minimization of fuel cost, emissions, and transmission losses. The standard IEEE 30-bus and 57-bus systems were employed to investigate the performance of the proposed algorithm. The simulation results were compared with those in the literature to show the superiority of the proposed algorithm over several other algorithms; however, the time computation of DA-PSO is slower than DA and PSO due to the sequential computation of DA and PSO. [ABSTRACT FROM AUTHOR]