Jagadeesh Pasupuleti

Power quality problems and solutions

This paper addresses the power quality problems and solutions related to (i) wind power generation and (ii) large industrial customers. Intermittent nature of wind power affects the quality of its electrical power output. The usage of low cost energy saving equipments also generates harmonics and voltage flicker thus affects the quality of supply voltage. A wide rang of solutions are being already proposed with the development on power electronic devices and electrical machines. This paper is organized to discuss the (i) power quality problems in brief, (ii) four major developments in wind generator technologies and (iii) solutions to large industrial customers using Dynamic Voltage Restorer (DVR), Active Power Filter (APF) and Static Compensator (STATCOM). Finally development in power electronic devices control is briefed in each of the devices, which has utilizes these power electronic devices with integrated solutions to solve number of power quality problems.

Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

This paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system’s output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model’s accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, −0.21 A (−4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy.

Battery Energy Storage System for PV Output Power Leveling

Fluctuating photovoltaic (PV) output power reduces the reliability in power system when there is a massive penetration of PV generators. Energy storage systems that are connected to the PV generators using bidirectional isolated dc-dc converter can be utilized for compensating the fluctuating PV power. This paper presents a grid connected energy storage system based on a 2u2009kW full-bridge bidirectional isolated dc-dc converter and a PWM converter for PV output power leveling. This paper proposes two controllers: a current controller using the d-q synchronous reference and a phase-shift controller. The main function of the current controller is to regulate the voltage at the high-side dc, so that the voltage ratio of the high-voltage side (HVS) with low-voltage side (LVS) is equal to the transformer turns ratio. The phase-shift controller is employed to manage the charging and discharging modes of the battery based on PV output power and battery voltage. With the proposed system, unity power factor and efficient active power injection are achieved. The feasibility of the proposed control system is investigated using PSCAD simulation.

Modeling of photovoltaic array output current based on actual performance using artificial neural networks

This paper presents prediction models for photovoltaic (PV) modules output current. The proposed models are based on empirical, statistical, and artificial neural networks. The adopted artificial neural networks are generalized regression, feed forward, and cascaded forward neural networks. The proposed models have two inputs, namely, solar radiation and ambient temperature, while systems output current is the output. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. These data are recorded every 10 seconds in order to consider the uncertainty of systems output current. Three statistical values are used to evaluate the accuracy of the proposed models, namely, mean absolute percentage error, mean bias error, and root mean square error. A comparison between the proposed models in terms of prediction accuracy is conducted. The results show that the generalized regression neural network based model exceeds the other models. The mean absolute percentage error, root mean square error, and mean bias error of the generalized regression neural network model are 4.97%, 5.67%, and −1.17%, respectively.

DFIG wind-turbine modeling with reactive power control integrated to large distribution network

The growth rate of penetration of wind energy has addressed many challenges and impacts related to the power quality and power system stability because of its intermittent nature. Once a doubly fed induction generator (DFIG) is connected to a power system, it absorbs lagging reactive power during the grid operation causing instability issues to the grid voltages. In this research paper, a DFIG based wind turbine is being modeled considering the optimal power coefficient and reactive power control strategy. The optimal power coefficient correlates the power law exponent with the respect of velocity and elevation. The reactive power control strategy is modeled using back-to-back converter system. The paper also investigates the voltage improvement of a large distribution network connected with DFIG system. By using steady state analysis, the voltage magnitude profiles are determined during DFIG at point of common coupling. The proposed control shows that DFIG based wind-turbine integration to the large distribution network enhances the weak bus voltage.

Modeling and simulation of InAs photodiode on electric field profile and dark current characteristics

This work reports the dark current density-voltage (J-V) characteristics and electric field profile of InAs photodiode with 100μm × 1μm cross sectional area at a temperature of 300K. The device structure was simulated using 2D SILVACO software and the model was used to determine all the optimum material physical parameters based on the parameters reported in other literatures. Dark current mechanisms, which include drift-diffusion current, generation-recombination current, trap-assisted tunneling current and band-to-band tunneling current, were incorporated into the ATLAS electrical characteristics model. Simulated dark current results were compared with the experimental results that were obtained from InAs photodiodes fabricated from molecular beam epitaxy (MBE) and metal organic vapor phase epitaxy (MOVPE) grown InAs wafers. Good agreement is found between the simulation and experimental results.

Data from renewable energy assessments for resort islands in the South China Sea.

Renewable energy assessments for resort islands in the South China Sea were conducted that involves the collection and analysis of meteorological and topographic data. The meteorological data was used to assess the PV, wind and hydropower system potentials on the islands. Furthermore, the reconnaissance study for hydro-potentials were conducted through topographic maps in order to determine the potential sites suitable for development of run-of-river hydropower generation. The stream data was collected for 14 islands in the South China Sea with a total of 51 investigated sites. The data from this study are related to the research article “Optimal combination of solar, wind, micro-hydro and diesel systems based on actual seasonal load profiles for a resort island in the South China Sea” published in Energy (Khan et al., 2015) [1].

Optimal combinations of PV, wind, micro-hydro and diesel systems for a seasonal load demand

This paper proposes multiple optimal combinations of hybrid renewable energy systems for a resort island based on actual generation-side energy auditing, assessment of seasonal renewable energy resources availability versus load profiles, and techno-economic analyses. Tioman Island is selected for this study as it represents the typical energy demands of many resort islands in the South China Sea. The island depends primarily on diesel-fuel for electricity generation. As a result, the generating company is exposed to diesel fuel volatile market prices and high operation and maintenance costs. Moreover, the diesel power plant poses possible risk of fuel spills and environmental degradation. Hence, to mitigate the diesel fuel dependence, an optimal combination of hybrid diesel and renewable energy is proposed. The study starts with an actual generation-side auditing, which includes distribution of loads, seasonal load profiles, and types of loads as well as an analysis of local development planning. Subsequently, surveys of available renewable resource potentials such as solar, wind, and hydro have been conducted that involved collection and analysis of meteorological data. Finally, HOMER software was used to perform techno-economic analyses for different combinations of hybrid energy system. Results of the analyses include the optimal hybrid system configurations, cost of the hybrid system, fuel saving, and CO2 emission reduction.

Micro-hydropower potential assessment and generation volatility due to seasonal climate

This paper proposes hydropower potential sites for a resort island located in the South China Sea. Furthermore, the effects of seasonal climate to hydropower generation are also discussed. The resort island selected is Tioman, as it represents the typical energy requirements of many resort islands in the South China Sea. The island relies mainly on diesel-fuel for electricity generation. However, diesel is subjected to high and volatile market prices, high operation and maintenance costs, and poses environmental risks. Therefore, to mitigate diesel fuel dependency, reconnaissance studies were conducted through topographic maps and hydrological studies in order to determine the potential sites or locations available for the development of a micro-hydropower plant in Tioman Island. The result shows that a total of 10 sites identified to have micro-hydro potential from 26 investigated sites. From the monthly estimated river flow, it can be seen that the hydropower in Tioman Island varies seasonally, having the highest potential during the northeast monsoon season.

Demand Response Impact on Market Operator's Revenue and Load Profile of a Grid Connected with Wind Power Plants

Economic properties of an integrated wind power plant (WPP) and the demand response (DR) programs in the sample electricity market are studied. Time of use (TOU) and direct load control (DLC) are two of the DR programs that are applied in the system. The influences of these methods and the incentive payments by market operators (MOs) with variable elasticity are studied. It is observed that DR with TOU and DLC programs together yields better revenue and energy saving for MOs.