Ahmad Alzahrani

Predicting Solar Irradiance Using Time Series Neural Networks

Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport’s photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other models and is better than the linear regression model. The use of additional meteorological variables, particularly sky cover, can further improve the prediction performance.

Analysis and design of bipolar Dickson DC-DC converter

This paper presents a novel converter based on the Dickson voltage multiplier for high voltage applications. This converter will have two Dickson chains connected differently to improve the original Dickson voltage multiplier. The main advantage of this converter is that the stresses on the capacitor will be reduced by half, and the output voltage ripples are reduced. The required energy storage of the capacitor is 30% of the original Dickson voltage switched capacitor converter. The analysis and design of this converter are presented and verified by simulation. A 20W prototype was implemented to validate the analysis and simulation. The results show that the proposed converter is an improved version of the original Dickson and modified Dickson converters and it has higher efficiency up to 94%.

A novel non-isolated high-gain dc-dc boost converter

In this paper, a novel interleaved boost converter topology is introduced. The proposed converter is based on a combination of two types of voltage multiplier cells (VMC): Dickson and Cockroft-Walton (CW) voltage multiplier. Connecting VMCs to an interleaved boost stage yields many benefits, including smaller ripples on the input current, reduced voltage stress across switches and capacitors, and high overall voltage gain. These advantages allow designers to design high gain dc-dc converters with low-rated and highly efficient semiconductor components. Moreover, the use of VMC can reduce the requirements of the required energy storage and also allow to design with smaller capacitors than the conventional boost converter. This paper presents the analysis of steady state voltage gain, the design of the converter, selection of the components, and simulation of the voltage and current stresses across switches and storage elements. Also, A 200 W prototype was implemented to convert 20 to 400 Vdc. The implementation details and major waveforms are included and illustrated.

Stochastic model for PV sensor array data

Recently, a number of researchers have investigated photovoltaic (PV) system modeling. Modelling a PV panel and its incident solar radiation to predict future trends improves a systems performance. This paper presents a fast, practical method that can be used to predict PV output power. By using present data of weather condition and present output power of the PV system, this predictor is modeled using linear regression analysis. The data from multiple sensors is collected only once before it is correlated to one sensor so that, in the future, only one sensor is needed to collect the data. Several experiments conducted under different weather conditions and different windows sizes of linear regression were completed to validate this method. These results were compared to the Meinel and Meinel model. This method yielded promising results, as the root mean square errors were low.

An interleaved non-isolated DC-DC boost converter with diode-capacitor cells

Using the conventional boost converter in applications that require high-voltage-gain conversion have several challenges. The conventional boost converter can have high-voltage-gain ratio if only operates with an extremely high duty ratio. Also, the output diode might suffer from the reverse recovery phenomena at the high duty cycle and high-power applications. Therefore, a new topology is required to overcome the issues associated with using the conventional boost converter in applications that require high-voltage-gain conversion. This paper introduces an interleaved boost converter with a diode-capacitor voltage multiplier cells to achieve high-voltage gain conversion with low voltage stresses on switches. The theory of operation of the proposed converter is explained. The steady-state analysis of proposed converter is verified through simulation to convert 20 to 400 VDC. A 200 W hardware prototype was experimentally implemented to validate both simulation and analysis.

Boost converter with bipolar Dickson voltage multiplier cells

This paper presents the analysis and design of a boost converter with bipolar Dickson voltage multiplier. The bipolar Dickson voltage multiplier offers high gain with low stress on both capacitors and switches. Connecting bipolar Dickson cell to a boost converter yields several benefits such as an overall high static voltage gain and at the same time reduces the required inductance that ensures operation in continuous conduction mode. The proposed converter is suitable for interfacing renewable energy sources because it has non-pulsating input current. The proposed converter was simulated to convert 20 V to 200 V. A 50 W hardware prototype was implemented using GaN active switches to validate the simulation and theory.

Chaotic behavior of DC-DC converters

This paper presents an investigation of the chaotic behavior of power electronic converters. The DC-DC converters are highly nonlinear devices. The switches and passive components make the behavior of these converters difficult to be characterized with the conventional analytical tools. Therefore, investigating modeling methods and analysis is necessary. Knowing the behavior of these converters allows improvement in the electromagnetic compatibility of the power supply, and enhances converter design process. Several tools to investigate the behavior of the converters are illustrated, such as bifurcation diagrams and Poincare maps. SIMULINK model is provided to illustrated chaos and bifurcation phenomena.

A novel interleaved non-isolated high-gain DC-DC boost converter with Greinacher voltage multiplier cells

This paper presents a novel interleaved high-gain DC-DC boost converter with Greinacher voltage multiplier cells (GVMC). The proposed topology consists of two stages: an interleaved boost stage to reduce the AC ripple on the input current and the voltage multiplier circuit to increase the voltage gain ratio. The voltage stress on active switches is lower than that from the conventional boost converter, which allows selection of efficient components. The interleaved boost stage contains two inductors and two switches with the capability of being fed by either a single or two sources. Steady-state analysis and design of active and passive components are illustrated. A 20 to a 400 Vdc converter model is simulated, and A 200 W, prototype is experimentally implemented to verify the theory and simulation.

Modeling and simulation of a microgrid using feedforward neural networks

Electric power grids and complex computer systems have many similar properties of the operation behavior and the structure. A microgrid can be treated as a small electric grid that contains consisted of numerous residential loads, energy storage units, and distributed energy. The goal of implementing microgrids is to supply power to homes even in the event of an electric grid outage. That is, the stored energy in the storage unit and distributed generation will supply energy to the load until the main grid return to the normal operation, and therefore, supply power to the load and store energy back to the storage unit. This method allows decentralization of the electric grid regarding control and energy supply. To deal with decentralized systems, one needs to construe the electric grid as a system of systems (SoS), and use models that can capture the dynamics of the microgrid. This paper presents a model of microgrid using feedforward neural networks. This model can be utilized in complex system modeling techniques such as agent-based approaches and system dynamics, or a combination of various methods to represent different electric elements. An example of modeling real microgrid is presented to demonstrate the emergent characteristics of the interconnected system. Simulation results and waveforms are discussed.