Design Considerations of Wireless MonitoringNetworks for Concentrated Photovoltaic Power

Plant ApplicationsWensi Wang, Victor Cionca, Donagh O’Mahony, Ningning Wang,

Mike Hayes, Brendan O’Flynn and Cian O’MathunaMicrosystems Group, Tyndall National Institute, Cork, Ireland

Email: wensi.wang@tyndall.ie

Abstract—Advanced monitoring systems of smart grids (SG)present significant growth potential for renewable energy gen-eration whilst low-cost and miniaturized wireless sensor nodesbring opportunities to replace large and expensive monitoringequipment. Renewable power systems required complicated mon-itoring and control. For example, a concentrated photovoltaic(CPV) power plant requires temperature, electric current andcomponent faults monitoring. All of these can be provided bywireless sensor networks (WSN).

However, the practical implementation of WSN solutions forCPV application remains largely unexploited. A gap exists inthe research of SG WSN monitoring system. Power system re-searchers lack understanding of the capability of state-of-the-artWSN systems, whilst the WSN researcher lack on understandingof the detailed application area of SG. This paper addresses thepractical issues encountered during the design of a SG WSN andan insight how this knowledge gap can be bridged.

It starts with introduction of WSN system with the conceptof constrained resources in wireless communication, energy anddata processing capability. Then, it presents the CPV WSNdesign architecture and issues ranging from network scalabil-ity, topology, sampling rate to sensor selection based on theconstrained resources in WSN. The target application scenariois 1MW scale CPV power plant with 40 CPV panels. Overall,this paper presents valuable design considerations along withan analysis of trade-offs for power plant level CPV monitoringsystem implementation based on IEEE 802.15.4 wireless sensorplatforms.

I. INTRODUCTION

Ubiquitous computing and wireless communication tech-nologies have been developing rapidly over the last twodecades. Starting from very basic point-to-point radio commu-nication, the evolution of wireless communication technologynow provides the means to develop more reliable data linkswithin multi-hop ad-hoc networks. At the same time, the rapiddevelopment of advanced sensors continuously minimize thesize and cost of modern sensing systems. The combination ofwireless communication and sensor technologies has made itpossible to transform high cost, wired-connected (or withoutcommunication capability) conventional industrial monitor-ing equipment into smart wireless sensor ad-hoc networks.With clear picture of industrial requirements and designatedstandardization activities, industrial wireless sensor networks(IWSNs) offer great potential to improve industrial systems by

providing low cost and reliable real-time monitoring/actuationwithin data networks [1].

Over the last 20 years, a radical change in global energygeneration has occurred. The adoption of renewable energyincreases at unprecedented pace [2]. In several countries,renewable energy already provides more than 10% of totalenergy generation [3]. The changing of energy generationat large scale in a relatively short period of time leads todifferent issues in the power grid [4]. Environmental-relatedenergy generation from renewable energy sources requireadditional monitoring, fault detection and diagnostics in futurepower grids [5]. However, in many countries for example theU.S., an average 60 years old power grid system lacks thetechnology necessary to perform automated monitoring andcontrol functionality [6]. Renewable energy such as PV isinherently more difficult to predict. This may cause severesafety and reliability challenges to the existing power grid.Among several types of photovoltaic energy generation tech-nologies, concentrated photovoltaic (CPV) technology pro-vides the highest conversion efficiency with tandem solar cells[7]. However, CPV modules require more complicated control(solar tracking, thermal management and fault detection). Thesolar tracking and thermal performance are distinct from thenon-concentrated based photovoltaic system [8], [9].

Many have suggested the utilization of IWSN in the areaof power grid monitoring and control as an inherent part ofthe “smart grid” [10], [5] and [11]. The concept of smart gridis broad and has only started to be implemented in recentyears. Many issues in the practical implementation have notbeen fully addressed [5]. Literature reviews reveal little orno information on the implementation of IWSN for CPV.This paper addresses the potential application of WSN inthe area of CPV power harvesting within the EU-Eniac ERGproject framework. ERG project focuses on the solar energysupply chain, starting form high efficiency power conversionto managing the energy distribution inside a smart grid, withthe target of different “grids” of applications.

As part of ERG research, this work introduces the practi-cal issues encountered during the implementation of a CPVWSN. A system architecture with detailed considerations inscalability, radio frequency (RF) performance and physical

deployment is proposed in this work. The network scale,

topology and monitoring frequency are discussed based on

the constrained resources of a typical WSN.

In section II, the wireless sensor network is introduced with

considerations of constrained resources. Section III analyses

the WSN design issues in the proposed CPV monitoring

system with constrained resources. Section IV concludes with

findings and recommendations.

II. RESOURCE CONSTRAINED WIRELESS SENSOR

NETWORKS

A. Overview

A typical wireless sensor network consists of a number of

wireless sensor nodes (motes). The mote is the most basic

unit in the system. It normally features 1) micro-controller

unit (MCU), 2) wireless communication unit, 3) sensors with

digital/analog interfaces and 4) power supply. A typical mote

(designed by Tyndall) layout and implementation in building

energy management application is shown in Fig. 1. The sensor

interfaces are I2C bus for digital sensors and 10-bit analog-

to-digital converter (ADC) for analog sensors. The micro-

controller adopted in the design is an Atmel1281 and the

RF module is a CC2420 radio chip. In this implementation,

Fig. 1. Wireless Sensor Node for Building Energy Management Application[12]

6 ADC sensor channels and 8 digital sensor channels are

available. The device is highly miniaturized with a dimension

of (H×W×D) 25mm×25mm×15mm excluding the power

supply.

B. Resource Constraints

The design of the CPV monitoring application based on

WSN has 2 main constraints in resources: 1) strength and

quality of the RF link; 2) power supply energy.

1) RF link strength and quality: In order to reduce en-

ergy consumption and prolong the expected lifetime, the RF

transmission power of WSN motes is one or two orders of

magnitude smaller than that of other wireless technologies

that share the 2.4GHz ISM band. WSN radios that are

compliant with the IEEE 802.15.4 standard will transmit at

approximately 0dBm (1mW), while WiFi (802.11) can go

up to 23dBm (200mW), which is the limit allowed by the

Federal Communications Commission (FCC) in the USA.

The outcome of this difference is that sensor networks are

limited in the area they can cover and they are susceptible to

interference from the more powerful radios. WSN links are

therefore unstable, which implies that:

• at local level, in point-to-point communication, packets

might have to be resent due to transmission failures

• at global, network level, the topology can change quickly,

blocking access to some nodes and forcing traffic through

other ones.

The instability of WSN links has a direct impact on power

consumption and, as such, a lot of effort has been put into

finding a good estimator of the link quality [13]. The best

estimators rely on measurements provided by the physical

(radio) layer, like signal-to-noise ratio (SNR), or link quality

indication (LQI). It has been shown [14] that SNR is, over

short periods of time, a more accurate indication of the link

status.

The SNR, in decibels, computed by the receiver, is equal to

the difference between the power of the received signal Pr and

the noise power Pn. The power of the received signal depends

on the transmission power Pt, the distance between sender and

receiver, the environment (walls, static and moving obstacles

will reflect and distort the signal creating a multipath effect)

and interference from other sources. There are many models

for the power of the received signal (and, implicitly, for the

signal to noise ratio), but the one that is most accurate is the

log-normal path loss model [15].

γ(d)mean = Pt − PL(d0)− 10 · ηlog10(

d

d0

)− Pn (1)

where γ(d) is the SNR as a function of distance, Pt is the

transmit power in dBm, PL(d0) is the path loss at distance

d0, η is the path loss exponent and Pn is the noise floor. It is

worth noting that due to multi-path effect, a Gaussian random

path loss variable N(0, σ) also exists in the path loss model

with a zero mean and σ standard deviation.

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Fig. 2. Packet receive rate(PRR) measured with various receive signalstrength indicator (RSSI)

It is common for links in a WSN to be asymmetrical - the

sender can reach the receiver but not vice-versa. A simple way

to combat such links is to require packet acknowledgments.

Figure 2 presents measurements of the percentage of packets

Fig. 3. Transmit Distance and SNR based on Tyndall Mole Measure mentand Radio Propagation Scenario at a electric-power substation [51

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target deployment scenario. At 500-lux, the power density ismeasured at 22p lV· cm- 2 on maximum power point (MPP).For a PV cell of similar size to a credit card (45cm2 ), thedaily average MPP power is 0.49mW. Based on previouswork, the power management circuit for sub- ImW energyharvesting has a conversion efficiency at approximately 81%in room temperature condition [201. The output power of theenergy harvester, with a IOFsuper-capacitor as energy storage,is 0.40mW. With the low output power. it is necessary toinclude sleep/wake-up scheme (duty cycle) in the sensing andtransmit in order to reduce system power consumption. Thepower limitation is a key factor that constrains the sensor type,sensing duty cycle, transmit power upper limit, e tc.

III. CPV WSN DESIGN ANALYSIS BASED ON RESOURCECONSTRAINS

A. System Architecture and ScalabilityThe IMW level CPV power plant is a 5-level power system

with approximately 40 CPV panels and covers more than15,000m2. The system layout with dimension and averagepower level is illustrated in Fig. 5.

The concentrated solar energy builds up heat on the high­efficiency, high-cost CPV cells. When exposed to high temper­ature for a prolonged period of time without effective cooling,the heat can permanently damage the cell. Thus, temperatureis a key parameter in CPV monitoring and it can be monitoredwith a WSN. Another function performed by WSN is samplingoutput power (I-V characterization) in order to record longterm performance data of CPV for lifetime and degradationsstudies. in CPV applications, the WSN mote can either bedirectly connected to the measurement point, or it can serveas a mother board to several wire connected sensor boards.Based on the two type of sensing applications, five monitoringconfigurations are proposed and presented in Table I.

The total number of motes and sensors are then calculatedin the targeted 1MW level CPV power plant scenario. Theparameters of this typical CPV power plant illustrated in Fig.5 are summarized in Table II. The results of the simulationare presented in Fig.6.

It can be observed from the simulation results that forIMW level power plant, it is unrealistic to perform cell levelmonitoring due to the very large number (> 120, 00Cl) of motes

Fig. 4. Measured power density of DSSC cells

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2) Energy Harvesting: Many have suggested applying en­ergy harvesting technologies to WSN systems in order toprolong the system lifetime and eliminate or reduce the fre­quency of battery replacement therefore reducing maintenancecost 1161, 117]. In smart grid applications, the requirementof energy harvesting is obvious. The regular maintenance forWSN modules on electric-power system is inefficient andlogically unrealistic. Harvesting ambient energy to sustain along system lifet ime is essential for future SG WSNs.

The most common ambient energy sources are solar energy,vibration energy, thermal energy and for SG application, elec­tromagnetic energy from power line [181. In this application.dye-sensitized solar cell (DSSC) is proposed as the energysupply for WSN. The power density of DSSC is simulated in[19]. At l -sun AMI.5 condition, state-of-the-art DSSC moduleor submodule conversion efficiency is approximately 8.2% 17).It is worth noting that the WSN module will be installed at theback of the CPV panel to reduce light intensity impact on theconcentrator directed at the sun. The light intensity in this typeof condition are measured at between 300lux (overcast day).700lux (full daylight. low ground reflectance) and IOOOlux(full daylight, high ground reflectance) in October. northwestEurope.

Fig. 4 presents the I-V characterisation of DSSC cell in

that were acknowledged (and thus successfully transmitted)between a sender and receiver at different distances, producingdifferent received signal strength and SNR values. The averagenoise floor was measured at -88dBm. The measurementsindicate that with an SNR > 9dB, over 95% of the packetsare successfully transmitted.

The model presented in Eq. I allows to estimate themaximum distance at which the 95% success rate can beachieved. A smart-grid environment is simulated by using theparameters for path loss exponent and shadowing deviationpresented by Gungor et ai lS] for a 500-kV substation. Theresults are presented in Fig. 3 and show that with line-of-sight(LOS) transmission at OdBm, 95% of packets are received ina SSm range; for non-line-of-sight (NLOS), which would bemore realistic for a CPV with panels orientated in differentdirections. at IOdBm, the limit is on average 30m.

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Fig. 5. CPV Power Plant System Layout

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Fig. 6. CPV WSN moe/sensor numbers simulation

TABLE IICPV SYSTEM CONHGURATI ON PARAMETm~s

With module level monitoring configuration and X2 = 5,the average distance between motes is 2 meters on the samepanel and 30 meters for inter-panel communication. Bothdistance allows reliable communication with 95% PRR asintroduced in last section. Multi-hop network is then suggestedin the communicatio n protocol to extend the communicationrange of motes far away from the central receiver (gateway)by including several communication relays.

B. Sensors and Mote Duty CycleThe temperature sensor in this application requires a mea­

surement range of _ 20°C to l 00aC. The PI-100 thermometercan provide an accurate (O. IOC ), low power and low costsolution in this temperature range. The same implementat ionwas presented in previous work with power consumptionmeasurement data [12]. The wireless sensor module consumesan average 55mW in active sensing and transmit mode, 2-3orders of magnitude higher than the energy harvesting powerfrom credit card sized PV cells. It is necessary to operate themote in low duty cycle mode in order to lower the averagepower consumption. Based on the energy harvesting powerconstraint, Eq. 2 shows the duty cycle limit of mote,

P~lp· ( I - D)+ Pact · D + krly · D· Prly::::: PEH (2)

where P~lp is the sleep mode power consumption, Pact isthe active mode (sensing and transmit) power consumption,D is the active mode duty cycle , kr 1y is the number ofrelay transmissions, Pr 1y is the relay transmission powerconsumption, PEH is the energy harvesting generated power.In this implementation, the number of relays from the mostremote CPV panel to the central receiver is 3. For the motesclose to the central receiver (in the substation), the powerconsumption is higher than the remote motes due to the relaytransmission. Thus, the duty cycle limit is decided by thepower balance in these motes. With 0.022mW sleep modepower consumption, 55mW active mode power consumption,70mW relay power consumption, 0.45mW average energyharvesting output power, 1 second active time in each sensingand transmit and relay transmission, the CPV WSN canachieve I measurement every 10 minutes (0=0. 16%). Withseveral dedicated relay motes with larger solar cell as powersupply (k r1y = 0 for other motes in Eq.2), the measurementinterval can be significantly reduced to I measurement every

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t n c: N o . OF CELLS IN A MODUL E; n m: N o . OF M ODUL ES IN A ZOl'iE ; nz:No . O F ZOSES IN A PASEL; n p: No. O F PAN~: LS IN A POWE R PLA NT;n , ,,:No . O F I- V CHAR ACf ERII..ATl ON SENSO RS; n,·f - l S: 1 MarE CONNECTS 1

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and sensors required. The module level monitoring wi th 1sensor and 1 mote on each module also requires 151 moles oneach panel. The most feasible implementation is module levelmonitoring with multiple sensors connected to single mote.With X 2 set to 5, the number of motes on one panel is 31. For40 panels, the total number of motes required by the systemis 1240.

2 minutes. The frequent measurements provided by the powerautonomous mote provides detailed data for CPV operationmonitoring and overheat detections.

IV. CONCLUSION

In this paper, the applicability of wireless sensor networksin smart grid monitoring systems, particularly for the con-centrated photovoltaic power plant is investigated. A practicalWSN system with constrained resources is introduced from theRF link strength/quality and power autonomous perspective.The analysis shows that an IEEE 802.15.4 compatible moteis capable of covering a transmit distance of 40 metres withPRR>95%. A credit card sized DSSC type solar cell providesthe monitoring mote with 0.45mW power when installed atthe back of CPV panels.

Based on the resource constraints, this paper investigatedthe application of WSN in a 5-level (plant-panel-zone-module-cell) 1MW CPV power plant scenario. The scale of thenetwork is studied with considerations in transmission rangeand cost issues. The study discovered that the most feasibleand cost effective solution is deploying sensors at modulelevel and connecting multiple sensors to one mote. In thisway, the number of measurement points and mote/sensor costis well balanced. The operation duty cycle of the mote isalso studied based on energy harvesting constraints and thepower consumption profile of motes in multi-hop networks.In the target scenario, the duty cycle is approximately 1measurement every 10 minutes without dedicated relay motesand 1 measurement every 2 minutes when relay motes areincluded.

Overall, the experimental and simulation results in this workconcludes that the wireless sensor networks provide a feasiblesolution for CPV power plant operation monitoring from bothRF transmission and power autonomous perspectives. Theemployment of the proposed WSN provides valuable data totrack the performance of CPV down to module level. Theintroduction of other key technologies, such as low-powercurrent sensors, would further accelerate the adoption of WSNin renewable energy based power plant monitoring systems.

ACKNOWLEDGMENT

This work was supported by ENIAC/JU project 2010(270722-2) ERG “Energy for a Green Society: from sustain-able harvesting to smart distribution. Equipments, materials,design solution and their applications”.

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