Arun Rajendran

Bio: Arun Rajendran is an academic researcher from Amrita Vishwa Vidyapeetham. The author has contributed to research in topics: Microgrid & Photovoltaic system. The author has an hindex of 4, co-authored 8 publications receiving 38 citations.

Power flow control of solar PV based islanded low voltage DC microgrid with battery management system

Abstract: The use of low voltage DC microgrids is a promising concept that makes improvements in power quality and reliability to end users through different control techniques. A coordinated control strategy is addressed within DC microgrid for proper power management between sources and loads and regulation of DC bus voltage. For this; different modes of operation are performed based on state of charge of battery and DC bus voltage. Droop control is an effective solution for power sharing between solar PV; battery and loads. A separate control strategy has been developed for battery charging and discharging. MATLAB/SIMULINK simulation results are presented to demonstrate the effectiveness of the proposed power control strategy with battery management system during various operating conditions.

A novel algorithm for power flow management in Combined AC/DC microgrid

Abstract: A Combined AC/DC microgrid is established for the efficient power flow between AC and DC loads and the sources combining the advantages of both AC and DC microgrids. The AC and DC microgrids are merged through an interlinking converter (IC). Energy storage element is also connected to the system for providing continuous power supply. In this paper a new control algorithm for the bidirectional power flow control of interlinking converter during islanding mode of combined AC/DC microgrid along with battery control is implemented. The modified droop based control strategy eliminates the use of communication links and also reduces the power conversion stages. The input to the power management strategy are the frequency of the AC microgrid and the DC bus voltage of interlinking converter. The experimental simulations are performed in MATLAB/ SIMULINK and results are analyzed.

Power flow control in low voltage AC microgrid using photovoltaic system and battery energy storage

Abstract: An ac micro grid mainly consists of distributed generators an energy storage system and loads where the coordinated power management between these elements should be properly done in order to ensure efficient utilization of renewable energy resources. Since the renewable energy sources are inherent sources of supply the energy storage system plays an important role in managing the power flow between each and every elements of the microgrid by keeping the SoC of the ESS under safe region. In this study an autonomous power flow is proposed which is done through bus signaling method and the SoC conditions of ESS. The proposed control algorithm uses local controllers for regulating the bus voltage and frequency of the microgrid. Hence basic power management and voltage and frequency regulation is done within the microgrid.

Performance enhancement of PV arrays under partial shading conditions using SEPIC converter

Abstract: Conventional techniques fail to guarantee successful tracking of the global maximum power point under partial-shading conditions. This results in significant reduction in the power generated as wells as the reliability of the photovoltaic energy production system. For the effective utilization of solar panel under partial shading condition, global maximum power point tracking method (GMPPT) is required. This paper discusses an improved perturb and observe technique for tracking global maximum power point of photovoltaic arrays that has better performance even under partial shading condition than the conventional tracking algorithms. Initially GMPP is located by adjusting the control signal and then the control moves to local MPP stage. For the present study, single ended primary inductance converter is used as the dc-dc interface for MPP Tracking. This is a buck-boost derived converter which is better suited for photovoltaic applications than conventional buck-boost converter. Solar panel has been modelled and partial shading effects are implemented. Simulation results showing the performance of modified algorithm are presented with the help of MATLAB/Simulink.

Numerical conic distance relay

Abstract: Relays are the key device that is used for the protection of a power transmission system. The paper presents the development of a numerical distance relay, based on conic characteristics. The developed distance relay is intended for the protection of transmission network so that the scheme would exclude most of the conditions for which tripping is undesirable (arc resistances and power swings). The Conic characteristics have been plotted and are tested by injecting faults. The characteristics which is developed contained the faults that occurred within the reach of the relay setting. The relaying logic is coded in MATLAB and the transmission system is simulated in Simulink. The result shows that the Conic is highly suitable for protection of medium, high and extra high voltage transmission line during faults.

Autonomous Control of Interlinking Converters with Energy Storages in Hybrid AC-DC Microgrids

Abstract: The coexistence of ac and dc subgrids in a hybrid microgrid is likely given that modern distributed sources can either be ac or dc. Linking these subgrids is a power converter, whose topology should preferably be not too unconventional. This is to avoid unnecessary compromises to reliability, simplicity, and industry relevance of the converter. The desired operating features of the hybrid microgrid can then be added through this interlinking converter. To demonstrate, an appropriate control scheme is now developed for controlling the interlinking converter. The objective is to keep the hybrid microgrid in autonomous operation with active power proportionally shared among its distributed sources. Power sharing here should depend only on the source ratings and not their placements within the hybrid microgrid. The proposed scheme can also be extended to include energy storage within the interlinking converter, as already proven in simulation and experiment. These findings have not been previously discussed in the literature, where existing schemes are mostly for an ac or a dc microgrid, but not both in coexistence.

A Bidirectional Power Charging Control Strategy for Plug-in Hybrid Electric Vehicles

Abstract: Plug-in Hybrid Electric Vehicles (PHEVs) have the potential of providing frequency regulation due to the adjustment of power charging. Based on the stochastic nature of the daily mileage and the arrival and departure time of Electric Vehicles (EVs), a precise bidirectional charging control strategy of plug-in hybrid electric vehicles by considering the State of Charge (SoC) of the batteries and simultaneous voltage and frequency regulation is presented in this paper. The proposed strategy can control the batteries charge which are connected to the grid, and simultaneously regulate the voltage and frequency of the power grid during the charging time based on the available power when different events occur over a 24-h period. The simulation results prove the validity of the proposed control strategy in coordinating plug-in hybrid electric vehicles aggregations and its significant contribution to the peak reduction, as well as power quality improvement. The case study in this paper consists of detailed models of Distributed Energy Resources (DERs), diesel generator and wind farm, a generic aggregation of EVs with various charging profiles, and different loads. The test system is simulated and analyzed in MATLAB/SIMULINK software.

Power flow control of solar PV based islanded low voltage DC microgrid with battery management system

Abstract: The use of low voltage DC microgrids is a promising concept that makes improvements in power quality and reliability to end users through different control techniques. A coordinated control strategy is addressed within DC microgrid for proper power management between sources and loads and regulation of DC bus voltage. For this; different modes of operation are performed based on state of charge of battery and DC bus voltage. Droop control is an effective solution for power sharing between solar PV; battery and loads. A separate control strategy has been developed for battery charging and discharging. MATLAB/SIMULINK simulation results are presented to demonstrate the effectiveness of the proposed power control strategy with battery management system during various operating conditions.

Modeling of a photovoltaic array in MATLAB simulink and maximum power point tracking using neural network

Abstract: Maximum Power Point Tracking (MPPT) is very useful tool in PV application. Solar radiation and temperature are the main factor for which the electric power supplied by a photovoltaic system varies. The voltage at which PV module can produce maximum power is called ‘maximum power point’ (or peak power voltage).1–3 The main principle of MPPT is responsible for extracting the maximum possible power from the photovoltaic and feed it to the load via dc to dc converter which steps up/steps down the voltage to required magnitude. Various MPPT techniques have been used in past but Perturb & Observe (P&O) algorithm is most widely accepted.4–6 P&O algorithm has also been shown to provide wrong tracking with rapidly varying irradiance.7–10 In this paper we are implemented neural network based MPPT method. Artificial Neural Network (ANN) is an artificial network that can able to mimic the human biological neural networks behavior. ANN widely used in modeling complex relationships between inputs and outputs in nonlinear systems. ANN can also be defined as parallel distributed information processing structure. The ANN consists of inputs, and at least one hidden layer and one output layer. These layers have processing elements which are called neurons interconnected together. To calculate error contribution of each neuron after a batch of data processing a method called ‘back propagation’ is used. Back propagation is commonly used by the gradient descent optimization algorithm to adjust the weight of neurons by calculating the gradient of the loss function. This technique is also called back propagation error. This is because the error is calculated at the output and circulated back through the network layers.11 Mathematical solar array modeling