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Implementation of wireless remote monitoring and control of solar photovoltaic

(PV) system

Conference Paper · September 2012

DOI: 10.1109/TDC-LA.2012.6319050

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Francisco J Galdeano Mantinan

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Marcelo Gustavo Molina

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Abstract--This paper describes the implementation of a

wireless remote monitoring and control system of a solar photovoltaic distributed generator (PV-DG) for microgrids applications. To this aim, a small-scale PV-DG system is implemented using a 1.28 kWp assembly of PV polycrystalline modules connected to a single-phase utility grid through a commercial inverter. Then, a flexible, robust and reliable measurement and control system based on wireless sensor network (WSN) architecture is deployed. The wireless communication technology utilizes a full duplex digital system using the ZigBee protocol, based on the IEEE 802.15.4 standard for Wireless Personal Area Network (WPAN). The supervisory control system is implemented on a digital signal processor (DSP) and a human-machine interface (HMI) software is developed for interacting with and managing remote sensor systems (RSSs).

Index Terms— Digital signal processor (DSP), IEEE 802.15.4 standard, microgrid, monitoring and control, solar photovoltaic distributed generation (PV-DG), remote sensor system (RSS), renewable energy, smart grid, wireless sensor network (WSN), ZigBee protocol.

I. INTRODUCTION HE depletion of global fossil fuel reserves in addition to the ever increasing environmental pollution have strongly

impelled during last decades the development of renewable energy sources (RESs). The need of having available sustainable energy generation systems for replacing gradually conventional ones requires the improvement of structures of energy supply based mostly on clean and renewable energy resources [1].

At present, solar photovoltaic (PV) generation is assuming increased importance as a RES application because of distinctive advantages such as simplicity of allocation, high dependability, absence of fuel cost, low maintenance and lack of noise and wear due to the absence of moving parts. Furthermore, the solar energy characterizes a clean, pollution-free and inexhaustible energy source. In addition to these factors are the declining cost and prices of solar modules, an increasing efficiency of solar cells, manufacturing-technology

This work was supported in part by the Argentinean National Agency for the Promotion of Science and Technology (ANPCyT) and the National University of San Juan (UNSJ) under Grant PICTO−UNSJ 2009, No. 0162.

M. E. Andreoni López and F. J. Galdeano Mantiñan are with the National University of San Juan, San Juan, J5400ARL, Argentina (e-mails: martinandreoni@gmail.com, fjgm85@gmail.com).

M. G. Molina is with the Argentinean National Council for Science and Technology Research (CONICET) and with the Institute of Electrical Energy (IEE) at the National University of San Juan, San Juan, J5400ARL, Argentina (e-mails: mgmolina@iee.unsj.edu.ar).

improvements and economies of scale. Even more, this trend is also expected to be increased, sustained by the development of new power electronics technologies, new circuit topologies and control strategies [2]-[3].

Over the last decade, an additional interest in connecting PV generation to the electricity grid has arisen, because of the many benefits of using renewable energy in distributed generation systems, including the strong support provided by governments of many countries, as investment subsidies and incentives. The in-situ, distributed, dispersed or decentralized generation (DG) is a concept that has been widely spread in recent years and is expected to become an important way to provide electricity in future power systems [4]-[5]. These generators are typically small-scale units (ranging from less than a kW to tens of MW) connected to the distribution network, sited at or near customers side to meet specific customer needs, to support economic operation of the distribution grid, or both. Thus, DG can reduce losses, enhance the possibly of using waste heat, and postpone investments in new transmission and large-scale generation systems. In addition, distributed generation may have additional valuable benefits, which have not yet been fully developed, such as exploiting clean energy generation using renewable sources, providing reactive power support, voltage regulation and the local contribution to the efficiency and reliability of the electric system, among others [6].

Incorporating advanced technologies, sophisticated control strategies and integrated digital communications into the existing electricity grid has initiated a new revolution in the power sector through the emergence of the Smart Grids (SGs). Smart grids have begun to radically change the way electricity is produced, consumed and distributed. A SG is an electricity transmission and distribution network that has the ability to rapidly assimilate, simplify and understand large amounts of information and use it correctly, by making intensive use of both, automation and information and communication technologies (ICTs) [7]. Smart grids, which are currently seen as the energy infrastructure of the future intelligent cities, rely on two-way digital communication technology between the utility companies and their demand (industrial and home consumers), so that the information provided by consumers can be used by companies to allow for more efficient operation of the electrical network and to reduce power consumption. In this way, smart grids allow consumers to efficiently manage their energy usage and select the more economically viable offers of power supply, to improve security, reliability and efficiency of the entire electrical

Implementation of Wireless Remote Monitoring and Control of Solar Photovoltaic (PV) System Martín E. Andreoni López, Francisco J. Galdeano Mantiñan, and Marcelo G. Molina, Member, IEEE

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delivery system through automation, and thus to enhance the grid integration of the more environmentally beneficial generation alternatives, including renewable energy resources and energy storage.

A major concept related to the smart grid paradigm for low voltage distribution systems are the grid-interactive AC microgrids (MGs). This novel network structure allows for the efficient use of distributed energy resources (DERs) (including distributed generation, renewable energy sources and distributed energy storage) and a cluster of loads using energy demand response (DR), also offering significant control capacities on its operation. In this way, the coordinated control of DERs and DR would allow to maximize the benefits for the owners of the microgrid, giving an attractive remuneration, as well as for the users, providing the thermal and electric demands to its local area by using advanced equipments and control methods [8]. This active distribution grid, which can be managed as if were a group with predictable generation and demand, usually operates connected to the main power network but can be autonomously isolated (islanded) during an unacceptable power quality condition. This promising grid architecture has been developed to address the integration of renewable energy sources, with the characteristics associated with limitations of renewable resources (mainly producing irregular and intermittent) and the lack of electricity infrastructure in their usual locations, and allow for the integration of electric vehicles connected to the grid [9]. Fig. 1 shows the general scheme of a smart microgrid with its energy Internet-like model, delivering real-time energy information and knowledge and empowering smarter energy choices with two-way communication, multi-directional power flow, and closed-loop automation at its core.

This paper aims to develop a wireless remote monitoring

and control system of a solar photovoltaic distributed generator (PV-DG) for smart microgrids applications. The contribution of this work is to demonstrate the development, testing, and utilization of the entire system using embedded Zigbee low-power mesh networking radio technology. Firstly,

it gives the proposed system overview of both hardware and software architecture and then the implementation and testing of the integrated system.

II. OVERVIEW OF THE PV-DG WIRELESS REMOTE MONITORING AND CONTROL SYSTEM

In order to better understand major issues in deploying a wireless remote monitoring and control system of a solar photovoltaic distributed generator, a small-scale prototype system that satisfies the requirements for microgrids applications have been developed. The proposed PV-DG wireless remote monitoring and control system is shown in Fig. 2 and consists mainly of two major blocks. One is the monitoring and control block for receiving data and the other is the transmitting blocks for collecting and sending data. To this aim, a small-scale PV-DG system is implemented using a 1.28 kWp (peak power) assembly of polycrystalline PV modules. The PV array implementation is composed of 2 strings of 8 modules, totalizing 16 modules of 80 Wp, model Solartec KS50 (built with Kyocera cells). The strings are shunt-connected to the dc link of a 1.2 kVA/1.2 kW/120 Vdc commercial inverter, model Sunny Boy 1200 from SMA Solar Technology. This dc-ac converter interfaces the array of PV panels with the 220 V/50 Hz single-phase utility grid and incorporates maximum power point tracking (MPPT) capabilities via its OptiTrac MPP-tracking process. This inverter can be used in the most diverse ac grids thanks to their galvanic isolation. It has an efficiency of more than 90% and outputs modified sine wave output voltage. The unit is equipped with extensive protection facilities such as overload, overheat and protection of PV panels.

The wireless remote monitoring and control system of the PV-DG system is deployed with a flexible, robust and reliable wireless sensor network (WSN) architecture. The proposed remote sensing comprises real-time measurement of both the inverter and the PV array variables. The data acquired from the inverter includes the output voltage, current, and active, reactive and apparent power supplied by the inverter to the ac grid. In the same way, the data obtained from the PV array are the dc voltage, current, and power delivered by all the panels to the inverter dc link. Similarly, meteorological variables such as wind speed, solar radiation, ambient temperature and average module back-surface temperatures are acquired. Temperature sensors employed for panels are resistant thermometers while precision integrated-circuit temperature sensors are employed for ambient temperature. The overall solar radiation is measured with a reference solar cell. An anemometer measures wind speed using cups that rotate around a mobile shaft at varying speeds.

The wireless communication technology utilizes a full duplex digital system using the ZigBee protocol, based on the IEEE 802.15.4 standard for Wireless Personal Area Network (WPAN) operating in the frequency of 2.4 GHz and supporting a maximum transmission speed of 250 Kb/s in distances up to 125 meters and configured as star, hybrid networks or peer-to-peer topologies [10]. The ZigBee protocol is designed so that if a number of different radio transceivers are deployed, the radios would automatically form a network without user intervention. The ZigBee protocol within the radios will take care of retries, acknowledgements and data

Fig. 1. General architecture of a smart microgrid with its energy Internet-likemodel

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message routing. ZigBee also has the ability to self-heal the network. If any of the radio transceivers is removed for some reason, a new path would be used to route messages from source to destination. Devices in the ZigBee specification can either be used as End Devices, Routers or Coordinators.

A ZigBee network can have two types of devices. Full Function Device (FFD) has full functionality and can act as network coordinator, router or even as a reduced function device (RFD). The coordinator (unique in the network) has the capacity of structuring the network, establishing an addressing scheme, keeping the address tables and is the only network element with the capacity for communicating with other networks; it also carries out the router functions. Routers also have to be able to forward messages, discover neighboring devices and build up a map of the routes to other nodes; normally they are active, communicating with network members. The Reduced Function Device communicates only with its network coordinator or near router, so it cannot carry out network coordinator functions. It is assumed to be one of the network ends and it acts or reads information from the physical medium (end point device); being its implementation simpler. This device normally runs in sleep mode, to save energy, and wakes up whenever it has to receive or send a message. The wireless network implemented in this work operates as a so-called mesh network, deploying 4 ZigBee radio devices. One device is configured as ZigBee Coordinator (ZC) (or master) and attached to a monitoring host computer and a graphical user interface. The other 3 devices are configured as ZigBee End Devices (ZEDs) (or slaves). ZEDs are responsible for measuring values of the PV modules, inverter and environment and transmitting them to the coordinator through ZigBee radio. The most capable device, the coordinator, forms the root of the network tree and receives data transmitted via the Zigbee radio link from all ZEDs and are transferred to a host computer through a USB connection.

The PV-DG supervisory control system is implemented on a digital signal processor (DSP) from the Microchip DSPIC

family (DSPIC 30F3013), as well as with the ZigBee Coordinator as with the ZigBee End Devices, and a human-machine interface (HMI) software is developed using the LabVIEW graphical development platform for interacting with and managing the remote sensor systems (RSSs).

III. WIRELESS SENSOR NETWORK (WSN) The coordination of sensing and control of the solar

photovoltaic distributed generator is most effectively managed by using a wireless sensor network. A sensor network is a group of specialized transducers with a communications infrastructure intended to monitor and record conditions at diverse locations. A hard-wired system from sensing modules to a base station is potentially more reliable, but has limiting features, such as taking extensive time and costs to install and to maintain. On the other hand, a wireless data communication system can provide dynamic mobility and cost free relocation. WSN has been widely adopted in various applications in agriculture and industry. It provides numerous opportunities to use wireless signal communication in smart PV-DG systems for microgrids applications. Moreover, using wireless sensor network in PV systems, it is possible to make more complex maintenance, such as predictive maintenance. A predictive maintenance comprises localization and definition of related faults. The quick detection of failures in a PV system is very important in any condition for avoiding energy losses due to malfunctions of the system [10].

A. Wireless Sensor Nodes The core of the proposed wireless remote monitoring and control system of the PV-DG is the WSN with its sensor nodes. A sensor network consists of multiple detection stations called sensor nodes, each of which is small, lightweight and portable. Sensor nodes are the essential element of wireless sensor networks. Each sensor node is equipped with a set of sensors (including signal conditioning devices when necessary) and actuators, a microcontroller unit (MCU), an RF radio transceiver and a power source. The sensor or transducer

Fig. 2. Scheme of the small-scale solar photovoltaic distributed generator (PV-DG) with the proposed wireless remote monitoring and control system for smartmicrogrids applications.

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generates electrical signals based on sensed physical effects and phenomena. A microcomputer processes and stores the sensor output. The transceiver, which here is wireless, receives commands from a central computer (base station) and transmits data to that computer.

1) Sensors and Conditioning Systems Various types of sensors have been employed in the PV-DG

system to measure dc voltage and current of monitored PV modules. The PV output dc voltage is measured with precision resistor voltage dividers connected to a signal conditioner, made up of a fifth-order low-pass filter topology cascading two Sallen-Key architecture stages with input buffering and a precision instrumentation amplifier. The PV array dc current is measured with a Hall-effect sensor linked to a signal conditioner. The non-intrusive method of measurement and the very low power loss are among the advantages of this type of sensor versus a current shunt resistor. On the other hand, the inverter output phase voltage and current are measured with measuring voltage and current transformers, respectively. These electrical devices convert primary electrical values, i.e. currents and voltages, into proportional and in-phase values that are adequate for the signal conditioning stage, which is similar to the topology previously presented. In addition, it maintains a high safety level due to the galvanic insulation between the primary and secondary circuits. Eventually, meteorological variables such as wind speed, solar radiation, ambient temperature and average module back-surface temperatures are measured with especial transducers.

Temperature sensors employed for panels are the well-known platinum resistance thermometers (PRTs) aka resistance temperature detectors (RTDs), such as PT100 sensors, which offer excellent accuracy over a wide temperature range (from -200 to +850°C). In the same way, precision IC temperature sensors, such as the LM35 of National Semiconductor Corporation, whose output voltage is linearly proportional to the Celsius (Centigrade) temperature, are employed for measuring the ambient temperature. This integrated circuit does not require any external calibration or trimming to provide typical accuracies of ±1/4°C at room temperature and ±3/4°C over a full −55 to +150°C temperature range. The solar radiation is measured with a polycrystalline reference solar cell from PV-Engineering GmbH. The reference cell does not measure global incoming irradiance, but it measures the part of the irradiance which can be used by flat window solar cells of identical material and identical packaging to produce electricity, i.e. the yield of a certain PV cell type. Conversely, the wind speed is measured with a precision-crafted stainless steel wind cups anemometer sensor from Davis Instruments, including both wind speed and wind direction sensors. Vane is hand-balanced for optimal stability and accuracy.

The signal processing and measurement for the inverter ac variables is performed using an electrical energy measurement integrated circuit (IC), model ADE77531 of Analog Devices. The ADE7753 is a single phase multifunction energy metering IC with di/dt current sensing input for high accuracy over large variations in environmental conditions and time. It incorporates two second-order Σ-Δ 10-channel 16-bit analog-to-digital (A/D) converters, a digital integrator, reference

circuitry, temperature sensor, an input analog anti-alias low-pass filter, an output digital low-pass filter and all the signal processing required to perform active, reactive, and apparent energy measurements, line-voltage period measurement, and rms calculation on the voltage and current. Furthermore, the device offers the chance of digital calibration for power, phase, and input offset. This IC also includes a serial peripheral interface (SPI) capable of communicating with the radio module.

The signal processing and measurement for the PV array variables and meteorological conditions is performed using a digital signal processor (DSP) from the Microchip dsPIC family (dsPIC 30F3013), which is described below.

2) ZED-side Microcontroller Unit (MCU) A cost-effective microcontroller unit (MCU) solution for

data acquisition is the DSP dsPIC 30F3013. This highly integrated device has a 16-bit Modified RISC processor with modified Harvard architecture, and incorporates a 12-bit 200 ksps A/D converter. This MCU also includes a digital output serial peripheral interface (SPI) capable of communicating with the RF radio module.

The signal processing and measurement of for PV array variables and meteorological variables is performed using the same digital signal processor (DSP) previously presented (dsPIC 30F3013 from Microchip). Since, in all these cases the transducer outputs is a voltage signal proportional to the measured parameter, the same signal conditioner is employed. This MCU also includes a serial peripheral interface (SPI) capable of communicating with the ZigBee RF radio module.

3) ZED Radio Transceiver Wireless sensor networks are an emerging technology for

low-cost, unattended monitoring of a wide range of applications. Their importance has been enforced by the delivery of the IEEE 802.15.4 standard for the physical and MAC layers and the ZigBee standard for the network and application layers. Besides the embedded communications capabilities, this platform has also been chosen to provide reliable signal conditioning to raw sensors and subsequent data processing.

As was previously described, an XBee PRO OEM RF module from MaxStream, Inc has been employed to build a low-cost low-power wireless sensor node. This chip is a true system-onchip for wireless sensor networking ZigBee/IEEE 802.15.4 solutions. It exploits the excellent performance of the leading RF radio transceiver supporting an 802.15.4 radio operating in the 2.4 GHz band and offering a 250 kbps bandwidth. The radio range varies with a maximum of 125 meters.

The wireless network implemented in this work operates as a so-called mesh network, deploying 4 ZigBee devices model XBee PRO from MaxStream, Inc. One device is configured as ZigBee Coordinator (ZC) (or master) and attached to the host computer or base station. The other 3 devices are configured as ZigBee End Devices (ZEDs) (or slaves) for measuring values from the PV modules, inverter and environment sensors and transmitting them to the Coordinator through ZigBee radio and eventually to the host computer.

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4) Power Source The power for each sensor node is drawn from a 3.6 V 650

mAh NiMH battery pack, built with a string of three AA-size 1.2 V 650 mAh units.

B. Supervision and Control Node

1) ZC-side Microcontroller Unit The MCU employed for data-driven decision-making

(DDDM) and control is the same used for the data acquisition of variables in the wireless sensor nodes. The microcontroller unit is a low-cost DSP from the Microchip dsPIC family (dsPIC 30F3013). It has been chosen as a cost-effective microcontroller unit (MCU) solution for supervision and control. This highly integrated device has a 16-bit Modified RISC processor with modified Harvard architecture, a 10-channel 12-bit 200 ksps A/D converter, 24-bit wide instructions, 16-bit wide data path, up to 30 MIPS operation and many other powerful DSP features. This MCU has a 24 kB on-chip Flash memory program space, 2 kB of on-chip data RAM, 1 kB of nonvolatile data EEPROM. It also includes various digital communication peripherals, such as 1 SPI (previously used), 2 universal asynchronous receiver/transmitters (UARTs) and 1 I²C (inter-integrated circuit). The UART usually does not directly generate or receive the external signals used between different items of equipment. UARTs are commonly used in conjunction with communication standards such as RS-232, RS-422 or RS-485. Thus, in this work a separate interface device (UART-RS232/USB bridge) is used to convert the logic level signals of the UART (RS232) to and from the external signaling levels of another standard, like the popular universal serial bus (USB). Most computer hardware now connects to a computer system via USB ports; consequently the UART/USB bridge allows easily attach the MCU to the host computer aiming at monitoring and controlling the wireless sensor nodes.

2) ZC Radio Transceiver The ZigBee radio transceiver attached to the host computer

is configured as the only Coordinator (or master) of the wireless network implemented in this work. This device is the same as the other 3 wireless sensor node transceivers, but with different settings. Here, the coordinator is a Full Function Device (FFD) which has full functionality and can act as network coordinator, router or even as a reduced function device (RFD). The coordinator has the capacity of structuring the network, establishing an addressing scheme, keeping the address tables and is the only network element with the capacity for communicating with other networks; it also carries out the router functions. It forms the root of the network tree and receives data transmitted via the Zigbee radio link from all ZEDs and are transferred to a host computer through a USB connection. It also has to be able to forward messages, discover neighboring devices and build up a map of the routes to other nodes; normally they are active, communicating with network members.

3) Human-machine Interface (HMI) Software The complete software is developed on two different

platforms consisting of back end embedded software coded in C which is run on the embedded platform (dsPIC 30F3013)

and a front end graphical user interface (GUI) implemented using the LabVIEW graphical development platform from National Instrument running on the monitoring and control host computer. Monitoring needs, regarding the hypothetical sensing devices that can be attached as well as the sampling interval required for each of them, have dictated the implementation of a high-level application protocol. For instance, every attached sensor can have different operating rules that must be transferred, stored and applied by each remote sensor device. In addition, power-management issues can be addressed to allow a controlled operation of the PV system, such as the operation in maximum power point conditions, the disconnection of the power grid when is required by the plant operator, the implementation of an intelligent failure detection system, among others.

The HMI software has been written in such a way that users of this program can have a clear and concise picture of the photovoltaic system performance at a glance. Currently, PV systems are still rather expensive to install. Hence, for average consumers to take the plunge and install such a system at home, they would want to know how the system is performing and how much power it is producing for them. Besides the reassurance that it offers, it can also ensure that if any situation is causing the photovoltaic system to not perform at its expected level, the owner can be informed early and remedies can be carried out. One scenario is that of branches or litter shading part or whole of the PV array. The power output will then differ from what the irradiation sensor suggests that it should be producing. The owner then can go and check on the photovoltaic array. To prevent owners from having to monitor the program constantly, the program is able to send a buzzing alarm to the owner when such triggering events occur, allowing them to take the necessary actions.

Data obtained from the main parameters of the PV array and weather conditions are stored in databases for their later analysis and processing. They include the inverter output voltage, current, and active, reactive and apparent power supplied to the ac grid. In addition, the voltage, current, and power delivered by all the panels to the inverter dc link. Similarly, meteorological variables such as wind speed, solar radiation, ambient temperature and average module back-surface temperatures. The monitoring data at each sensing node can be published using a web server. In this way, the web server permit the user of the internet to access dynamic web pages associated with the stored values using a browser.

IV. IMPLEMENTATION AND TESTING The proposed embedded hardware and software modules

have been built and integrated to evaluate the performance of the wireless remote monitoring and control system of a PV-DG. The proof-of-concept system implemented to demonstrate its feasibility was composed of the PV system equipped with Zigbee-enabled End Devices, a Coordinator, and a monitoring Host Computer, as seen in Fig. 2.

Fig. 3 depicts a screenshot of part of the developed program, where the PV system electrical variables and weather conditions are being processed, stored and plotted, as shown in Fig. 3(a) and Fig. 3(b) respectively. As can be noted, the implemented GUI is in Spanish language at this beta stage.

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V. CONCLUSION

In this paper, a low power consumption and cost-effective PV-DG wireless remote monitoring and control system using Zigbee technology is introduced. A human-machine interface (HMI) software is developed for interacting with and managing remote sensor systems (RSSs). The graphical user interface (GUI) implemented in the LabVIEW graphical development platform is used to monitor the real-time voltages, currents, and powers, and the state of the PV system, as well as the environmental conditions. Tests were performed to evaluate the response of the small-scale PV plant under several environmental conditions. The results of experiments on the system allow proving a concept of a promising low-cost wireless system solution for remote performance monitoring, fault detection and distributed control of smart photovoltaic systems. Potential applications of the ZigBee network technology in renewable energy systems can be extended to other energy sources and home energy solutions, and other fields of the future smart grid.

VI. REFERENCES [1] M. Kadiri, R. Ahmadian, B. Bockelmann-Evans, W. Rauen, R. Falconer,

“A review of the potential water quality impacts of tidal renewable energy systems,” Renewable and Sustainable Energy Reviews, vol. 16, no. 1, pp. 329-341, 2012.

[2] N. Femia, G. Petrone, G. Spagnuolo, M. Vitelli, “Optimal control of photovoltaic arrays,” Mathematics and Computers in Simulation, to be published, doi: 10.1016/j.matcom.2012.05.002.

[3] J. M. Pacas, M. G. Molina, E. C. dos Santos Jr., “Design of a robust and efficient power electronic interface for the grid integration of solar photovoltaic generation systems”, International Journal of Hydrogen Energy, vol. 37, no. 13, pp. 10076-10082, 2012.

[4] W. El-Khattam, M. M. A. Salama, “Distributed generation technologies: definitions and benefits,” Electric Power Systems Research, vol. 71,

pp. 119-128, 2004. [5] J. M. Guerrero, F. Blaabjerg, T. Zhelev, K. Hemmes, E. Monmasson, S.

Jemei, M. P. Comech, R. Granadino, J. I. Frau, “Distributed generation: toward a new energy paradigm,” IEEE Industrial Electronics Magazine, vol. 4, no. 1, pp. 52-64, 2010.

[6] S. Rahman, “Going green: the growth of renewable energy,” IEEE Power & Energy Magazine, pp. 16-18, Nov./Dec. 2003.

[7] M. Wissner, “The Smart Grid – A saucerful of secrets?,” Applied Energy, vol. 88, no. 7, pp. 2509-2518, 2011.

[8] B. Kroposki, R. Lasseter, T. Ise, S. Morozumi, S. Papatlianassiou, N. Hatziargyriou, “Making microgrids work,” IEEE Power & Energy Magazine, vol. 6, no. 3, pp. 40-53, 2008.

[9] M. G. Molina, P. E. Mercado, “Power Flow Stabilization and Control of Microgrid with Wind Generation by Superconducting Magnetic Energy Storage,” IEEE Trans. on Power Electronics, vol. 26 no. 3, pp. 910-922, 2011.

[10] P. Baronti, P. Pillai, V. W. Chook, S. Chessa, A. Gotta, Y. F. Hu,. “Wireless sensor networks: A survey on the state of the art and the. 802.15.4 and ZigBee standards,” Computer Communications, vol. 30, no. 7, pp. 1655-1695, 2007.

[11] F. Salvadori, M. de Campos, R. de Figueiredo, C. Gehrke, C. Rech, M. A. Spohn, A. C. Oliveira, “Monitoring in industrial systems using wireless sensor network with dynamic power management,” IEEE Trans. on Instrumentation and Measurement, vol. 58, no. 9, pp. 3104-3111, 2009.

Martín E. Andreoni López was born in San Juan, Argentina. He graduated as Electronic Engineer from the National University of San Juan (UNSJ), Argentina, in 2011. He is currently working toward the Master Degree in Electrical Engineering at the Federal University of Rio de Janeiro, Brazil. His main research interests include instrumentation, data processing, communication, power electronics,

renewable generation and control systems.

Francisco J. Galdeano Mantiñan was born in San Juan, Argentina. He graduated as Electronic Engineer from the UNSJ, Argentina, in 2011. He is presently working as Automation Engineer in AustralTek and AMPEXA, Argentina. His main research interests include data processing, communication, power electronics, and control systems.

Marcelo G. Molina (S´01, M´05) was born in San Juan, Argentina. He graduated summa cum laude as Electronic Engineer from the National University of San Juan (UNSJ), Argentina, in 1997, and received the Ph.D. degree from the UNSJ in 2004. During 2004, he was a Doctoral Research Fellow at the Federal University of Rio de Janeiro, Brazil, supported by CAPES. From 2005 to 2007, he worked

as a Post-Doc Research Fellow from the Argentinean National Council for Science and Technology Research (CONICET) at the Institute of Electrical Energy (IEE), UNSJ. In 2009, he was a Visiting Professor at the University of Siegen, Germany, sponsored by DAAD, and in 2010 at the Federal University of Rio de Janeiro, Brazil, supported by CAPES. In 2004, he became an Assistant Professor with tenure at the UNSJ, and he was promoted to Associate Professor of Electrical Engineering in 2011. Since 2008, Dr. Molina is an Associate Researcher of the CONICET and in 2011 he became Deputy Director of the IEE at the UNSJ. His research interests include, power system modeling, analysis and control, power electronics and electrical drives, microgrid and smart grid technologies and distributed energy resources with emphasis on distributed and renewable generation and the grid-interactive application of energy storage.

He has been involved in many national and international research projects and in high-level consulting with the industry. He is the author or coauthor of more than 130 publications in his research fields including ISI/SCOPUS journal and conference proceeding papers, books and book chapters.

Dr. Molina a member of the IEEE Power Engineering Society, the IEEE Power Electronics Society and the Brazilian Power Electronics Society (SOBRAEP), among others.

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Fig. 3. Screenshot of the designed HMI software. (a) Electrical parameters.(b) Meteorological parameters.

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