This paper proposed a fractional order PID sliding mode control (FOSMC-PID) for speed regulation of permanent magnet synchronous motor (PMSM). Fractional calculus has been incorporated in sliding ...mode controller (SMC) design to enhance chattering suppression ability. However, the design of fractional sliding surface is crucial to ensure that speed tracking accuracy is not jeopardized. The proposed controller is designed with a fractional order PID sliding surface, which balances the characteristics of sliding surface with PI or PD structure in terms of robustness and dynamic performance of the controller. By simulation, speed tracking is proven to be faster and more robust with the proposed controller compared to SMC with integer order. Both integration and derivative terms in the surface design outperform FOSMC-PI and FOSMC-PD in terms of disturbance rejection and chattering. Experimental validation proves the advantage of the proposed controller in terms of robustness.
•A particle swarm optimization with hybrid iteration strategy; Switch-PSO is proposed.•Switch-PSO adaptively switches between asynchronous and synchronous update.•The switching is done according to ...the gBest.•A superior performance is observed for both CEC2014 and IIR modeling problem.
Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. Traditional PSO iteration strategies can be categorized into two groups: synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the performance of the entire swarm is evaluated before the particles’ velocities and positions are updated, whereas in A-PSO, each particle's velocity and position are updated immediately after an individual's performance is evaluated. Previous research claimed that S-PSO is better in exploitation and has fast convergence, whereas A-PSO converges at a slower rate and is stronger at exploration. Exploration and exploitation are important in ensuring good performance for any population-based metaheuristic. In this paper, an adaptive switching PSO (Switch-PSO) algorithm that uses a hybrid update sequence is proposed. The iteration strategy in Switch-PSO is adaptively switched between the two traditional iteration strategies according to the performance of the swarm's best member. The performance of Switch-PSO is compared with existing S-PSO, A-PSO and three state-of-the-art PSO algorithms using CEC2014's benchmark functions. The results show that Switch-PSO achieves superior performance in comparison to the other algorithms. Switch-PSO is then applied for infinite impulse response model identification, where Switch-PSO is found to rank the best among all the algorithms applied.
In conventional coil-based wireless power transfer (WPT) systems, achieving strong coupling and maximizing flux path is difficult without increasing coil dimensions. Also, the possibility of avoiding ...the presence of a null zone region where mutual inductance between coils becomes zero and later increases in the negative is unavoidable. Also, the amount of copper consumed increases the coil resistance, ultimately lowering the quality factor. To work on these issues, this article works on a novel coil design that overcomes the issues above to compete with conventional coils. The proposed coil utilizes circular and square shapes with intermediate coil widths and an optimum number of turns to maximize coupling, flux path support, shift the null zone to the extreme edge of the coil, reduce overall self-inductance, and offer low coil resistance. Thus, increasing the misalignment tolerance against the conventional circular and square coil and offering better performances. In addition, the operational characteristics of coils and its boundary conditions and magnetic field strength are analyzed using ANSYS Maxwell and finite element analysis (FEA). A 400 W system with LCC-S network is used to verify overall said performances at 200 mm air-gap. The maximum dc-dc efficiency thus achieved is <inline-formula> <tex-math notation="LaTeX">\sim 93\% </tex-math></inline-formula>.
Virtual synchronous generator (VSG) is an important concept toward frequency stabilisation of the modern power system. The penetration of power electronic-based power generation in power grid reduces ...the total inertia, and thus increases the risk of frequency instability when disturbance occurs in the grid. VSG produces virtual inertia by injecting appropriate active power value to the grid when needed. This virtual inertia can stabilise the grid frequency in case of a power imbalance between generation and loads or any disturbances that affected frequency stability. Its intensive research can see the importance of VSG in inertia control and various intelligent controller techniques. Owing to the importance of VSG in the modern power grid, this study provides a comprehensive review on the control and coordination of VSG toward grid stabilisation in terms of frequency, voltage and oscillation damping during inertia response. A review on the type of energy storage system used for VSG and their benefits is also presented. Finally, perspective on the technical challenges and potential future research related to VSG is also discussed in this study.
Abstract-Model predictive control has emerged as a powerful control tool in the field of power converter and drive's system. In this article, a weighting factor optimization for reducing the torque ...ripple of induction machine fed by an indirect matrix converter is introduced and presented. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponding to minimum torque ripple. However, model predictive torque and flux control of the induction machine with conventionally selected weighting factor is being investigated in this article and is compared with the proposed optimum weighting factor based model predictive control algorithm to reduce the torque ripples. The proposed model predictive control scheme utilizes the discrete phenomena of power converter and predicts the future nature of the system variables. For the next sampling period, model predictive method selects the optimized switching state that minimizes a cost function based on optimized weighting factor to actuate the power converter. The introduced weighting factor optimization method in model predictive control algorithm is validated through simulations and shows potential control, tracking of variables with their respective references and consequently reduces the torque ripples corresponding to conventional weighting factor based predictive control method.
To achieve a more sustainable supply of electricity and reduce dependency on fuels, the application of renewable energy sources-based distribution systems (DS) is stimulating. However, the ...intermittent nature of renewable sources reduces the overall inertia of the power system, which in turn seriously affects the frequency stability of the power system. A virtual synchronous generator can provide inertial response support to a DS. However, existing active power controllers of VSG are not optimized to react to the variation of frequency changes in the power system. Hence this paper introduces a new controller by incorporating GA-ANFIS in the active power controller to improve the performance of the VSG. The advantage of the proposed ANFIS-based controller is its ability to optimize the membership function in order to provide a better range and accuracy for the VSG responses. Rate of change of frequency (ROCOF) and change in frequency are used as the inputs of the proposed controller to control the values of two swing equation parameters, inertia constant (J) and damping constant (D). Two objective functions are used to optimize the membership function in the ANFIS. Transient simulation is carried out in PSCAD/EMTDC to validate the performance of the controller. For all the scenarios, VSG with GA-ANFIS (VOFIS) managed to maintain the DS frequency within the safe operating limit. A comparison between three other controllers proved that the proposed VSG controller is better than the other controller, with a transient response of 22% faster compared to the other controllers.
In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, ...all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.
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•A rotation invariant waste bin level classification system is introduced.•The location and orientation of the bin are detected using Hough line detection.•The waste level in a bin is ...classified as empty, partially full or full using SVMs.•The system detects the bin and classifies its waste level accurately.
In this paper, a solid waste bin detection and waste level classification system that is rotation invariant is presented. First, possible locations and orientations of the bin are detected using Hough line detection. Then cross correlation is calculated to differentiate the true bin position and orientation from those of other similar objects. Next, features are extracted from the inside of the bin area and together with detected bin corners they are used to determine the bin’s waste level. A few features are also obtained from the outside of the bin area to check whether there is rubbish littered outside the bin. The proposed system was tested on shifted, rotated and unrotated bin images containing different level of waste. In the experiment, bin detection was treated separately from waste level classification. For bin detection, if 95% of the opening area is captured then the bin is considered detected correctly. The waste level classification is only considered for the correctly detected bins where the waste level is classified as empty, partially full or full. The system also checks the presence of rubbish outside the bin. In training, only images containing unrotated bin were used while in testing images containing both unrotated and rotated bin were used in equal number. The system achieves an average bin detection rate of 97.5% and waste level classification rate of 99.4% despite variations in the bin’s location, rotation and content. It is also robust against occlusion of the bin opening by large objects and confusion from square objects littered outside the bin. Its low average execution time suggests that the proposed method is suitable for real-time implementation.
An adaptive gravitational search algorithm (GSA) that switches between synchronous and asynchronous update is presented in this work. The proposed adaptive switching synchronous–asynchronous GSA ...(ASw-GSA) improves GSA through manipulation of its iteration strategy. The iteration strategy is switched from synchronous to asynchronous update and vice versa. The switching is conducted so that the population is adaptively switched between convergence and divergence. Synchronous update allows convergence, while switching to asynchronous update causes disruption to the population’s convergence. The ASw-GSA agents switch their iteration strategy when the best found solution is not improved after a period of time. The period is based on a switching threshold. The threshold determines how soon is the switching, and also the frequency of switching in ASw-GSA. ASw-GSA has been comprehensively evaluated based on CEC2014’s benchmark functions. The effect of the switching threshold has been studied and it is found that, in comparison with multiple and early switches, one-time switching towards the end of the search is better and substantially enhances the performance of ASw-GSA. The proposed ASw-GSA is also compared to original GSA, particle swarm optimization (PSO), genetic algorithm (GA), bat-inspired algorithm (BA) and grey wolf optimizer (GWO). The statistical analysis results show that ASw-GSA performs significantly better than GA and BA and as well as PSO, the original GSA and GWO.12
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