Duy C. Huynh

Development and Comparison of an Improved Incremental Conductance Algorithm for Tracking the MPP of a Solar PV Panel

This paper proposes an adaptive and optimal control strategy for a solar photovoltaic (PV) system. The control strategy ensures that the solar PV panel is always perpendicular to sunlight and simultaneously operated at its maximum power point (MPP) for continuously harvesting maximum power. The proposed control strategy is the control combination between the solar tracker (ST) and MPP tracker that can greatly improve the generated electricity from solar PV systems. Regarding the ST system, the paper presents two drive approaches including open- and closed-loop drives. Additionally, the paper also proposes an improved incremental conductance algorithm for enhancing the speed of the MPP tracking of a solar PV panel under various atmospheric conditions as well as guaranteeing that the operating point always moves toward the MPP using this proposed algorithm. The simulation and experimental results obtained validate the effectiveness of the proposal under various atmospheric conditions.

Global MPPT of solar PV modules using a dynamic PSO algorithm under partial shading conditions

This paper proposes a novel global maximum power point tracking (MPPT) strategy for solar photovoltaic (PV) modules under partial shading conditions using a dynamic particle swarm optimisation (PSO) algorithm. Solar PV modules have non-linear V-P characteristics with local maximum power points (MPPs) under partial shading conditions. In order to continuously harvest maximum power from solar PV modules, it always has to be operated at its global MPP which is determined using the proposed dynamic PSO algorithm. The obtained simulation results are compared with MPPs achieved using the standard PSO, and Perturbation and Observation (P&O) algorithms to confirm the effectiveness of the proposed algorithm under partial shading conditions.

Maximum power point tracking of solar photovoltaic panels using advanced perturbation and observation algorithm

An efficient maximum power point tracking (MPPT) scheme is necessary to improve the efficiency of a solar photovoltaic (PV) panel. This paper proposes an advanced perturbation and observation (P&O) algorithm for tracking the maximum power point (MPP) of a solar PV panel. Solar PV cells have a non-linear V-I characteristic with a distinct MPP which depends on environmental factors such as temperature and irradiation. In order to continuously harvest maximum power from the solar PV panel, it always has to be operated at its MPP. The proposed P&O algorithm can reduce the main drawbacks commonly related to the P&O algorithm. This is achieved with determining the short-circuit current before each perturbation and observation stage. The obtained simulation results are compared with MPPs achieved using the conventional P&O algorithm under various atmospheric conditions. The results show that the advanced P&O algorithm is better than the conventional P&O algorithms for tracking MPPs of solar PV panels. Additionally, it is simple and can be easily implemented in digital signal processor (DSP).

Parameter estimation of an induction machine using a dynamic particle swarm optimization algorithm

This paper proposes a new application of a dynamic particle swarm optimization (PSO) algorithm for parameter estimation of an induction machine. The dynamic PSO is one of the PSO variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO as linear time-varying parameters. The acceleration coefficients are varied during the evolution process of the PSO to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The algorithm uses the measurements of the three-phase stator currents, voltages, and the speed of the induction machine as the inputs to the parameter estimator. The experimental results obtained compare the estimated parameters with the induction machine parameters achieved using traditional tests such as the dc, no-load, and locked-rotor tests. There is also a comparison of the solution quality between a genetic algorithm (GA), standard PSO, and dynamic PSO. The results show that the dynamic PSO is better than the standard PSO and GA for parameter estimation of the induction machine.

On-line parameter estimation of an induction machine using a recursive least-squares algorithm with multiple time-varying forgetting factors

This paper proposes a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors for on-line parameter estimation of an induction machine (IM). The regressive mathematical model of the IM is also introduced which is simple and appropriate for online parameter estimation. The estimator inputs using the proposed RLS algorithm are easily measurable variables such as the stator voltages and currents as well as the rotor speed of the IM. The simulation results obtained compare the estimated parameters with the IM parameters achieved using other RLS algorithms such as a standard RLS algorithm and a RLS with a constant forgetting factor. The comparison shows that the proposed RLS algorithm is better than others for on-line parameter estimation of the IM.

Advanced particle swarm optimisation algorithms for parameter estimation of a single-phase induction machine

This paper proposes a new parameter estimation approach for a single-phase induction machine (SPIM) whose parameters are usually obtained using several traditional techniques such as the DC, no-load, load and locked-rotor tests. The proposal is based on using two advanced particle swarm optimisation (PSO) algorithms. In the PSO algorithm, the inertia weight, cognitive and social parameters and two independent random sequences are the main parameters which affect the search characteristics and convergence capability, as well as the solution quality in each application. Two advanced PSO algorithms, known as the dynamic particle swarm optimisation (dynamic PSO) and the chaos particle swarm optimisation (chaos PSO) algorithms modify the algorithm parameters to improve the performance of the standard PSO algorithm. The algorithms use the experimental measurements of the currents and active powers in the SPIM main and auxiliary windings as the inputs to the parameter estimator. The experimental results obtained...

Energy efficient control of an induction machine using a chaos particle swarm optimization algorithm

This paper proposes a new application of a chaos particle swarm optimization (PSO) algorithm for loss model-based energy efficient control of an induction machine (IM) using an optimal rotor flux reference. The chaos PSO algorithm with a logistic map has been used for initializing a random value of the rotor flux reference, the inertia weight and two independent random sequences in the velocity update equation of the PSO algorithm. These result in the best convergence capability and search performance for the PSO algorithm in searching for an optimal rotor flux reference for energy efficient control of the IM. Additionally, this paper also proposes a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors for on-line parameter estimation of the IM. The estimated parameters are used to update IM parameter variations during operation. This means that the energy efficient control scheme is robust to parameter variations. Simulation results confirm the effectiveness of the proposed energy efficient control strategy.

Comparison between open- and closed-loop trackers of a solar photovoltaic system

Solar energy is one of the renewable energy sources which is widely used to provide heat, light and electricity. The solar tracking controller used in solar photovoltaic (PV) systems to make solar PV panels always perpendicular to sunlight. This approach can greatly improve the generated electricity of solar PV systems. There are popularly two drive approaches including open- and closed-loop drives. This paper analyses and compares the open- and closed-loop trackers of a solar PV system. The obtained experimental results are to validate the effectiveness of each tracker.