Saad Mekhilef

Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter

This paper presents simulation and hardware implementation of incremental conductance (IncCond) maximum power point tracking (MPPT) used in solar array power systems with direct control method. The main difference of the proposed system to existing MPPT systems includes elimination of the proportional-integral control loop and investigation of the effect of simplifying the control circuit. Contributions are made in several aspects of the whole system, including converter design, system simulation, controller programming, and experimental setup. The resultant system is capable of tracking MPPs accurately and rapidly without steady-state oscillation, and also, its dynamic performance is satisfactory. The IncCond algorithm is used to track MPPs because it performs precise control under rapidly changing atmospheric conditions. MATLAB and Simulink were employed for simulation studies, and Code Composer Studio v3.1 was used to program a TMS320F2812 digital signal processor. The proposed system was developed and tested successfully on a photovoltaic solar panel in the laboratory. Experimental results indicate the feasibility and improved functionality of the system.

An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation

This paper proposes an improved maximum power point tracking (MPPT) method for the photovoltaic (PV) system using a modified particle swarm optimization (PSO) algorithm. The main advantage of the method is the reduction of the steady- state oscillation (to practically zero) once the maximum power point (MPP) is located. Furthermore, the proposed method has the ability to track the MPP for the extreme environmental condition, e.g., large fluctuations of insolation and partial shading condition. The algorithm is simple and can be computed very rapidly; thus, its implementation using a low-cost microcontroller is possible. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging conditions, namely step changes in irradiance, step changes in load, and partial shading of the PV array. Its performance is compared with the conventional Hill Climbing (HC) method. Finally, an experimental rig that comprises of a buck-boost converter fed by a custom-designed solar array simulator is set up to emulate the simulation. The soft- ware development is carried out in the Dspace 1104 environment using a TMS320F240 digital signal processor. The superiority of the proposed method over the HC in terms of tracking speed and steady-state oscillations is highlighted by simulation and experimental results.

Modified Incremental Conductance Algorithm for Photovoltaic System Under Partial Shading Conditions and Load Variation

Under partial shading conditions, multiple peaks are observed in the power-voltage (P- V) characteristic curve of a photovoltaic (PV) array, and the conventional maximum power point tracking (MPPT) algorithms may fail to track the global maximum power point (GMPP). Therefore, this paper proposes a modified incremental conductance (Inc Cond) algorithm that is able to track the GMPP under partial shading conditions and load variation. A novel algorithm is introduced to modulate the duty cycle of the dc-dc converter in order to ensure fast MPPT process. Simulation and hardware implementation are carried out to evaluate the effectiveness of the proposed algorithm under partial shading and load variation. The results show that the proposed algorithm is able to track the GMPP accurately under different types of partial shading conditions, and the response during variation of load and solar irradiation are faster than the conventional Inc Cond algorithm. Hence, the effectiveness of the proposed algorithm under partial shading condition and load variation is validated in this paper.

Novel Vector Control Method for Three-Stage Hybrid Cascaded Multilevel Inverter

A three-stage 18-level hybrid inverter circuit and its innovative control method have been presented. The three hybrid inverter stages are the high-, medium-, and low-voltage stages. The high-voltage stage is made of a three-phase conventional inverter to reduce dc source cost and losses. The medium- and low-voltage stages are made of three-level inverters constructed using cascaded H-bridge units. The novelty of the proposed algorithm is to avoid the undesirable high switching frequency for high- and medium-voltage stages despite the fact that the inverters dc sources are selected to maximize the inverter levels by eliminating redundant voltage states. Switching algorithms of the high- and medium-voltage stages have been developed to assure fundamental switching frequency operation of the high-voltage stages and not more than few times this frequency for the medium-voltage stage. The low-voltage stage is controlled using SVM to achieve the reference voltage vector exactly and to set the order of dominant harmonics as desired. The realization of this control approach has been enabled by considering the vector space plane in the state selection rather than individual phase levels. The inverter has been constructed, and the control algorithm has been implemented. Test results show that the proposed algorithm achieves the claimed features, and all major hypotheses have been verified.

Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method

In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions.

A Fast-converging MPPT Technique for Photovoltaic System under Fast Varying Solar Irradiation and Load Resistance

Under fast-varying solar irradiation and load resistance, a fast-converging maximum power point tracking (MPPT) system is required to ensure the photovoltaic (PV) system response rapidly with minimum power losses. Traditionally, maximum power point (MPP) locus was used to provide such a fast response. However, the algorithm requires extra control loop or intermittent disconnection of the PV module. Hence, this paper proposes a simpler fast-converging MPPT technique, which excludes the extra control loop and intermittent disconnection. In the proposed algorithm, the relationship between the load line and the I-V curve is used with trigonometry rule to obtain the fast response. Results of the simulation and experiment using single-ended primary-inductor converter showed that the response of the proposed algorithm is four times faster than the conventional incremental conductance algorithm during the load and solar irradiation variation. Consequently, the proposed algorithm has higher efficiency.

Long-Term Wind Speed Forecasting and General Pattern Recognition Using Neural Networks

Long-term forecasting of wind speed has become a research hot spot in many different areas such as restructured electricity markets, energy management, and wind farm optimal design. However, wind energy with unstable and intermittent characteristics entails establishing accurate predicted data to avoid inefficient and less reliable results. The proposed study in this paper may provide a solution regarding the long-term wind speed forecast in order to solve the earlier-mentioned problems. For this purpose, two fundamentally different approaches, the statistical and the neural network-based approaches, have been developed to predict hourly wind speed data of the subsequent year. The novelty of this study is to forecast the general trend of the incoming year by designing a data fusion algorithm through several neural networks. A set of recent wind speed measurement samples from two meteorological stations in Malaysia, namely Kuala Terengganu and Mersing, are used to train and test the data set. The result obtained by the proposed method has given rather promising results in view of the very small mean absolute error (MAE).

Digital Control of Three Phase Three-Stage Hybrid Multilevel Inverter

Three-stage 18-level hybrid inverter design with novel control method is presented. The inverter consists of main high-, medium-, and low-voltage stages connected in series from the output side. The high-voltage stage is a three-phase, six-switch conventional subinverter. The medium- and low-voltage stages are made of three-level subinverters constructed by H-bridge units. The proposed control strategy assumes a reference input voltage vector and aims to approximate it to the nearest inverter vector. The control concept is based on holding the high-voltage state as long as it is feasible to do so. The reference voltage vector has been represented in a 60°-spaced two axis coordinate system to reduce the computational effort. The concept of the staged-control has been presented, the transformed inverter vectors and their relation to the switching variables have been defined, and the implementation process has been described. The test results verify the effectiveness of the proposed strategy in terms of computational efficiency as well as the capability of the inverter to produce very low distorted voltage with low switching losses.

Modified Model Predictive Control of a Bidirectional AC–DC Converter Based on Lyapunov Function for Energy Storage Systems

Energy storage systems have been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper proposes a modified model predictive control (MMPC) method based on the Lyapunov function to improve the performance of a bidirectional ac-dc converter, which is used in an energy storage system for bidirectional power transfer between the three-phase ac voltage supply and energy storage devices. The proposed control technique utilizes the discrete behavior of the converter, considering the unavoidable quantization errors between the controller and the control actions selected from the finite control set of the bidirectional ac-dc converter. The proposed control method reduces the execution time delay by 18% compared with the conventional model predictive control. Moreover, the nonlinear system stability of the proposed MMPC technique is ensured by the direct Lyapunov method and a nonlinear experimental system model. Detailed experimental results with a 2.5-kW downscaled hardware prototype are provided to show the efficacy of the proposed control system.

Dual Vector Control Strategy for a Three-Stage Hybrid Cascaded Multilevel Inverter

This paper presents a voltage control algorithm for a hybrid multilevel inverter based on a staged-perception of the inverter voltage vector diagram. The algorithm is applied to control a three-stage eighteen-level hybrid inverter, which has been designed with a maximum number of symmetrical levels. The inverter has a two-level main stage built using a conventional six-switch inverter and medium- and low- voltage three-level stages constructed using cascaded H-bridge cells. The distinctive feature of the proposed algorithm is its ability to avoid the undesirable high switching frequency for high- and medium- voltage stages despite the fact that the inverter’s dc sources voltages are selected to maximize the number of levels by state redundancy elimination. The high- and medium- voltage stages switching algorithms have been developed to assure fundamental switching frequency operation of the high voltage stage and not more than few times this frequency for the medium voltage stage. The low voltage stage is controlled using a SVPWM to achieve the reference voltage vector exactly and to set the order of the dominant harmonics. The inverter has been constructed and the control algorithm has been implemented. Test results show that the proposed algorithm achieves the desired features and all of the major hypotheses have been verified.