Real time Power Capping with Smart Circuit Breakerto maximize Power Utilization of Local Generator
Hiroyuki Ikegami* , Raja Vara Prasad Yerra** , Pachamuthu Rajalakshmi** and Hiroshi Esaki**

Graduate School of Information Science and Technology, University of Tokyo, Email: [ikegam, hiroshi]@wide.ad.jp**
Department of Electrical Engineering, IIT Hyderabad, India, Email: [ee10p004, raji]@iith.ac.in

Abstract—Effective energy management and control is an
important and urgent issue in the emerging and developing
countries, so as to achieve their sustainable growth, because ofpoor quality of power supply by their electric power companies.
In order to come up with the frequent electric power outage
by the power company, most of buildings in developing andemerging countries install a power generator. Although because ofpoor control system in the premises, utilization factor of output
capability of power generators is typically low except at peak
periods. To improve the utilization factor of power generator,
we propose a system, which can manage power segments in thebuilding using SCB (Smart Circuit Breaker). SCBs are connectedby wireless technologies with battery backup, and set their power
capping based on the indication issued by central manager. The
central manager computes power capping threshold of each SCB
using the proposed algorithm, in real-time fashion. Experimental
results show that the proposed algorithm can optimize therequired capacity of the local power generator and that we needa feedback-looped adaptive threshold calculation algorithm.
Keywords—Smart Buildings, Power capping, Green ICT.

I.

I NTRODUCTION

Most of commercial and residential buildings in emerging
and developing countries have on-the-premises backup power
generator against the outage of power supply by electrical
power company. These power generators runs with fossil energy sources, that leads to larger carbon foot print, investment
and maintenance costs. Though renewable energy sources likesolar energy system is widely considered as the alternate
backup power source, the recent studies, e.g., [4], reveals thatthe penetration ratio of renewable energy sources are very low,
because of large initial investment. Therefore, conventional
generators shall be a primary systems at least for next fouror five years. This means that the efficient and smart useof power generators minimizes the total carbon foot print
and operational costs of power generators. Traditionally, thecapacity of power generator is determined as the summationof total breaking capacities/thresholds of edge circuit breakers
in a building. For example, in a 220V system, if the thresholdof each breaker is 10 Amps and 100 circuits are existing in abuilding, 1000 Amps and 220kVA output is simply required forthe capacity of local power generator. Generally, the demandis lower than circuit breakers capacity, except at peak periods.
If generator was not planned against for the peak demand ofbuilding, the power supply system in the building is getting
unstable. In other words, unless we schedule or power capping
the loads of the system components during peak period, wecannot guarantee the stability of power system in the building.

Main contribution of this research is to improve utilization
factor of power generator in a case of power outage caused byelectric power company, by the combination of dynamic power
capping to Smart Circuit Breaker (SCB) system installed in
the building. We propose that the conventional circuit breakers
should be replaced by SCB in this paper. SCB can dynamicallydefine its power capping level, according to the control indication message by the central manager, when the building is in
power outage. Here, the proposed SCB system can also workin a peak demand period. SCB based power capping system
has an effective feedback system among central manager, SCB
and end users in a building. Since the proposed system canrun both in power outage and peak period, we can reducethe capacity of power generator or can reduce the amount of
power generation to improve its operational performance, asa result. Rest of the paper is organized as follows. Section II
describes related works regarding power capping and demand
control. Section III introduces the requirements and design ofproposed SCB-based power system. Section IV describes thecalculation algorithm of power capping level at every SCB.
Section V gives the results of experimentation. Finally, section
VI concludes the paper.
II.

R ELATED W ORKS

Power management capping is an evolving research area
especially in computer data centers [11], [13] and [5]. Many
data centers use some specific actuators such as UPS and CPU
clock speed control to enable power capping, that is sometimes
called as DCIM (Data Center Infrastructure Management).
Power capping in a chip and in a fabric level is also wellinvestigated, e.g. [12] and [8]. However above researches
mainly focus on a computer domain and it is difficult to apply
these frameworks for commercial and residential buildings.
There are many researches to improve the operational
efficiency of generated power in Smart Grid area. Welldesigned and sophisticated system based on state-of-the-art
technologies try to reduce power consumption, try to improve
power supplying efficiency or try to improve the operationalreliability of power system in a building. Smart Device andSmart Power Strip are two recent findings to reduce energy
consumption in a building. As discussed in [10] and [14],massive deployment of smart power strips can significantly
reduce the power consumption. Here, the functionality of smart
plug strip is to control power supply of conventional device
by the computer networks, so as to achieve Demand Response(DR), within a building. Many researchers, such as [7], [3]TABLE I: Comparison between SCB, Smart Device and Smart
Power Strip to make Power capping system#Devices in Home
#Devices in Commercial Building
Battery
Fineness
Deployability

End Devices

Smart Device

Smart
Strip

Power

Under 200
Around 1000
to 10000Not
considered
High
Low

Under 200
Around 1000
to 10000Not
considered
MediumMediumSCB
Under 5Around 10 to100
Considered
Low
High

Power Distribution Network
SCB - 1

Electrical Sub Network - 2

SCB - 2

Electrical Sub Network - N

SCB - NPower Distribution Line
Communication Line••

Main Electrical Panel

Power Supply

Electrical Sub Network - 1

Power Supply

Local Generator

SwitchPower Company

Set thresholds
Get current flow

Master Controller Check Power Source

Fig. 1: Proposed power capping system architectureand [2], are working on designing communication and control
protocols for DR using Smart Devices.
Their research objective is decreasing power consumption
in a building for ordinary operation. On the contrary, ourresearch purpose is to maximize the operational efficiency
of on-site power generator in a building. One circuit breakeraccommodates one circuit and around 10 devices under thecircuit in a typical configuration. A typical power distributing
panel may accommodate about 30 circuits. This means that asingle SCB manages around 300 devices. Number of batteries
for SCB system discussed in this paper is less compared tothe other approaches using Smart Device and Smart Power
Strips. TABLE I shows the comparison of the proposed system,
against similar conventional researches.
III.

P OWER C APPING A RCHITECTURE

This section describes the proposed power capping system.
Fig. 1 shows the overview of the proposed system. Mainelectrical panel is connected with both power company’s power
supply and local power generator, so as to select and switch
the appropriate power source for the building. All electrical
sub networks in a building are connected to main electrical
panel to be fed the power. Every sub network is monitored
and controlled by SCB, where all SCBs receive data from themaster controller using an available network, such as WiFi
and ZigBee. Master controller monitors the power sources,
i.e., power company and local generator and the current flows
on each SCB. Based on these electrical power status, theproposed system decide the power capping threshold of every
single SCB. Master controller runs power capping algorithms
to calculate the threshold value of each SCB, so as to guarantee
the current flow to every single sub networks in the building.
A. Requirements for the proposed SCB system
The following four are the requirements that the proposed
SCB system must satisfy for the proposed system.

1) Latency of Response: The latency of response against
the change of system status must be less than ten seconds.
Because the recent power generator automatically, without any
indication by human-being, starts the power supply within theorder of few seconds[6]. The system must complete all theconfiguration of SCBs in this time limitation.
2) Human activity: The system should be able to influence
the residents’ activity and should be able to refer to theresidents’ activity for control. Since the power consumption
in a building is strongly related to human activity, the systemshould have a feedback-loop so as to be referred the status ofbuilding by the residents, so that the residents react to control
their power consumption in the building. SCB-based powersystem proposed in this paper does not care about residents
and their activity to calculate the power capping thresholdvalues of SCBs, but only visualizes the status of building
and sub networks. Also, the calculation algorithm of power
capping threshold values of SCBs should be transparent to all
the residents in the building, so that the resident can realize
how the threshold values are determined. For example, theresidents may be able to inform to the system operator which
sub networks are critical or important and which sub networks
are not of critical. This means that the residents can inform to
set the priorities of each SCB and the system operator can refer
this priority into the threshold value calculation algorithm.
3) Island mode of SCB operation: Each SCB in thebuilding must work correctly even if the SCB loses network
connectivity. In the power outage time, there is a possibilityof network failure due to the lack of power supply to routersand switches. The system must work and keep power capping
functioning even during the failure of network system.
4) Power backup: SCB must have a power backup capability in order to come up with the situation where threshold of
power capping by the SCB is indicated by the central manager(”Master Controller” in Fig. 1) during the power outage.
B. Protocol and Interface Design
We adopt connectionless communication, rather than connection oriented communication with master-slave periodical
polling discipline to the communication protocol between the
Master Controller and each SCBs, in order to save the electricity consumption on SCB. Since the clock synchronizationand maintenance of accurate clock management at SCB is noteasy, we adopt the periodic polling by master node (Mater
Controller). Although, we adopt the polling methodology, theslave node (SCB) can let sleep (i.e., turn off) it’s network
interface for long period and can let wake up (i.e., turnon) it’s network interface so as to wait the reception ofpolling message from master node. Since large number of
SCB are accommodated in the building and the ratio of datatransmission period versus the data transmission interval isenough small, the period, where the network interface can beturned off, can be enough long.
Fig. 2 shows the common packet format among SCBs
and Master Controller. The size of packet is not fixed, but isvariable length, which is shown in the second byte field. The
first one byte represents the start of packet and the third byte
represents the type of packet. The last byte in the packet isthe checksum value, calculated by XOR excluding initial byte

••
Payload (variable length) ••STX|LEN|TYP| … |CHK

STX … 0b11111111
LEN … Packet Length
TYP … Type of Packet
CHK … Checksum

Fig. 2: Common Packet Format for Smart Circuit Breaker
Query P Mode|Config 1| … | Config N
Notification
Payloads

Ack.

Empty

Status
Empty
Query
Information
Ack. SCB Mode|P Mode|Info 1| … | Info N
Payloads

Configuration (Config)
TH1|SW1|TH2|SW2|TH3|SW3
Information (Info)
Config layout is same as above
Config(6 vals)|Ave.|Max|Dive.

Fig. 3: Payloads for 4 types of messages of the SCB system(0xFF). As shown in Fig. 3, we define four 4 types of packet
messages. These are query and corresponding acknowledgment messages for notification and status reporting functions.
Query message is generated by the Master Controller andacknowledgment message is returned from SCBs to the Master
Controller. The key point of this design is the information for
SCB is in the notification query.
Notification message is to set the power capping threshold
and the switch states (open/close) of the SCB. Query message
has two fields; power grid status field (P Mode in the Fig. 3)
and configuration fields (Config). Configuration field indicatesthe number of circuits. When the SCB has two circuits, thecontent of the configuration field is two. These six values are
stored in the memory on SCB as three pairs of thresholdvalues and switch states. These three pairs are referred bythree important events which are the first pair for the present
time, the second pair for the power outage and the third pair
for the time out. SCB changes threshold value and switch
state immediately and the first pair, when SCB receives a
notification query. Similarly, SCB changes to the second andthe third pairs when a power outage and network time out
event occurs respectively.
Status information message is for the Master Controller tocalculate the threshold values and the switch states for each
SCB depending on their recent states. Payload of status information query is empty as shown in Fig. 3. Status information
acknowledgment has three kinds of fields; the SCB mode, thepower grid status (P Mode, same as notification message) andthe circuit information (Info). In the circuit information field,
there are eight values; three pairs of threshold values and theswitch states along with the maximum and average current in
RMS (Root Mean Square). Master Controller calculates threshold values and determine the switch state for each SCB basedon the acknowledgment against this message. The current
RMS values are reset when SCB sends the status information
acknowledgment message. Master Controller sends the status
information query every five minutes and expects the message
of maximum current flow every five minutes.
Periodical polling saves a lot of energy consumption onslave devices, i.e., SCB. Slave device sends data only when
it receives a query message from the Master Controller.
Moreover we can control power supply for a wireless unit
because of the polling frequency is static. Here, we show
the Master Controller skeleton design in Fig. 4. The update
function includes the algorithms to decide new threshold values⃗
1: procedure C ONTROLLER (update, SCB,Interval)
2:▷ update is user defined function
3:
while 1 do
⃗ o ←getStatusInformation(SCB)
⃗
4:
Inf
⃗
⃗
5:
N ewStatus ← update(Inf o)⃗⃗
6:
sendNotification(SCB,
N ewStatus)
7:
Sleep(Interval)
8:end while
9: end procedure

Fig. 4: Controller skeleton: algorithm is given as update⃗ is a set of SCBs.
function and SCB
for every single SCBs. In this paper, we implemented two typesof update algorithms which are called as ”Simple” and ”Simple+ Adaptive” for evaluation purpose. Algorithms are describedin detail in next section. Finally we support the requirementsin the starting of this section with below statements.
1) Latency of Response: The completion of threshold value
calculation at the Master Controller and the configuration
setting of power capping value at SCB is unsynchronized.
When a power outage happened, the threshold value and switch
state for power outage timing is already stored on SCBs
inherently from notification message. SCB simply changes its
own threshold value and switch state immediately based on
the second values in the configuration field.
2) Human activity: In the current proposed system, thehuman activity is not considered in the calculation of thresholdvalues foe every SCB. However, the proposed system has thevisualization function of system status and behavior, so that theresidents may correctly and adequately react against the current
status of their building. And, one of other important reaction
by the residents is to avoid exceeding smaller threshold value
in SCB. The resident can inform inappropriate threshold value
setting to the system operator, so that a better parameters canbe configure in the threshold value calculation.
3) Island mode of SCB operation: Master Controller canrecognize the threshold values and the switch states of unreachable SCBs. Since each SCB has a timeout field andthe threshold values, which are set by Master Controller, andthese information are shared with Master Controller, Master
Controller can control the reachable SCB based on theseinformation so that the system can be operated safely.
4) Power backup: Since the SCB system adopts the masterslave periodical polling discipline, the SCB system can run
with low energy consumption because the communication unit
in SCB can be off in most of time, except the given timing ofpolling by Master Control node.
IV.

M ATHEMATICAL M ODELING AND C ONTROLLINGA LGORITHMS

SCB operates using communicated threshold values fromthe controller. Threshold is decided by the controller fromthe data analysis algorithms on the monitored values of thecircuit. Threshold can be calculated in different ways, but thealgorithm developed in our implementation is explained withmathematical expressions in the following subsections.
Pg = (1 + α)PC(1)

initialize PR = PLg , Priorities
Number of SCB  i = N

Wait till Next timeslot in block out
periodInitialize with “simple algorithm”
values or update PSCB vector
No

i>1
Yes
PC (i) < PR

No

Yes
PSCB(i) = PC (i)PR =PR - PSCB(i)i=i-1

Fig. 5: Flow chart for priority based Simple + Adaptivealgorithm
Pg > PC ≥N∑

pc (i) > PLg

(2)

i=0

V.

PC , Pg and PLg are maximum values of connected load,
power company, power generator respectively and α is thepercentage reserve of the system shown in Eq. 1 and 2. It
is assumed that power company supports maximum values ofconnected load in the building and power generator capacity
is less than Pg and PLg shown in the Eq. 2. PSCB (1),PSCB (2) ... PSCB (N) are threshold values of N networks
respectively. pc (1), pc (2) ... pc (N) are individual maximum
values of connected loads of same N networks respectively
meeting individual constraints of each SCB shown in the Eq.
2. Objective of the proposed SCB system shown at Eq. 3 is tominimize the overall threshold of the building without violating
constraints on each individual network as well as total capacityof the generator. Threshold of each SCB can be decided withdifferent methods like load forecasting, simple maximum value
and machine learning methods. The simplest of all the methods
is Simple which considers L immediate past values of the
network before power outage and computes the maximum
from it. The method does not consider any load variationsin the network during power outage period. Simple method is
experimented on our prototype system and it is observed thatloads in the network are frequently switching ON/OFF due
to addition of extra loads. Extra loads in the power outage
period for a sub network will exceed the threshold decided
during power from company.

min

N
∑

PSCB (i)

i=0

s.t.

PLg >

N
∑

(3)
PSCB (i)i=0

PSCB (i) < pc (i), i = 1, . . . , N.
P1 > P2 > P3 ......... > PN

assigned as integer number according to essential and non
essential loads during power outage period shown in Eq. 4. P1 ,P2 , ... PN are priorities of N networks or SCBs. In this priority
based method, thresholds are modified in real time according
to the load variations in each sub network and the priorities.
Detailed flow of the second algorithm is given in the flow
chart shown in Fig. 5. The algorithm starts with initializationof all SCBs with max value during normal operation andprioritizing them. Threshold of higher priority SCB is set firstand depending on PR other SCB thresholds are assigned. PC
is the observed demand on each network in previous time slot
and PR is the residual capacity of the generator after master
controller allocate SCB a threshold value shown in the Fig.
5. Above Simple + Adaptive method is implemented in our
prototype and it is observed that load instability issue due to
new incoming loads on priority networks is effectively handledshown in Fig. 8 and 9. Detailed discussion of above algorithm
and experimentation details are followed in next subsections.

(4)

Another method, Simple + Adaptive is proposed in this paper to counter some of the instability issues in Simple method.
This method considers the max value in Simple and predefinedpriority of each sub network. Priorities of each networks are

I MPLEMENTATION OF T HE SYSTEM AND
E XPERIMENTATION

A prototype system is developed to evaluate our proposedsystem. We carried out some experimentation scenarios on the
prototype with Simple max value (i.e., Simple) and Simple +Adaptive controller algorithms.
A. Implementation
A SCB with two ports is developed for the experimentation
shown in Fig. 6. Two CTs (Current Transformer; U RD CTL10-CLS) are connected to ADC (Analog Digital Converter)
ports. MCU (Micro Computing Unit; ATMEL ATmega328p)
captures ADC values every 1msec. using the internal timer
and calculate the RMS values every 100ms. MCU checks thecomputed RMS values to apply the power capping againstelectrical flow of each SCB based on their threshold values.
Two SSRs (Solid State Relay; SHARP S202S02) are used as
the power switching devices and each SSR connected to the
MCU is controlled by it. MCU is also connected to the XBee
module (XBee-ZB S2, 2.4GHz ZigBee Model) via UART
(Universal Asynchronous Receiver Transmitter) port. According to the threshold values set by Master Controller. SCB controls SSRs. SCB also updates maximum/average/divergencevalues on their memory for the sending status information
acknowledgment. MCU continuously checks for data from
XBee on UART port. MCU processes the data and sends an
acknowledgment message to the Master Controller using XBee
if the data is available on UART port. A 40mAh battery anda charge controlling IC are integrated into the MCU powerline to support during power outage time. The battery keeps
working for one hour. Master-slave periodical polling systemsaves energy consumption and contributes that tiny battery is
sufficient even for one hour power outage.
A prototype of SCB-based system is shown in Fig. 6 and 7.
As shown in Fig. 7, power source board, electric panel board
with SCBs and end circuits board are placed from right toleft. Controller PC has two USB devices which are connected
to power outage detector to recognize the status of a powercompany and a XBee to communicate SCBs. In the prototype
system, power is supplied from two power sockets. We definedthat left side socket is for local generator and right side is for

2)
3)
4)

Fig. 6: Two ports type Smart Circuit Breaker
Demand SidePower Supply Side

4 SCBs2 SCB Communication Units

Left: Local Generator
Right: Power Company

Controller
Power Outage Detector

From top of right toleft of bottom SCB-1, 2, 3, 4

• Power Outage Detector isconnected to here
• Communication Unit isalso attachedFig. 7: Prototype of SCB-based Power System: Upper image
shows over whole deployment and lower image shows moredetail information and each components
power company. A CT is attached to the main cable to detect
the power outage which we termed as power outage detector.
Power outage event is detected when the main cable does nothave current flow in it. The SCB board has four SCBs, each
SCB pair has one MCU board. We named these SCBs as SCB1, SCB-2, SCB-3 and SCB-4 from top of right to bottom ofleft. In real deployment, all of SCBs are in an electrical panel.
Visualization system is also implemented on Master Controller computer. We customized a third party software namely
InfluxDB[1] and Grafana[9]. InfluxDB is a kind of databasesystem and designed to store huge time-series data and Grafana
is a visualization system worked on web browser for timeseries data. Residents can monitor the real time thresholdvalues that can be configured SCBs through this visualization
system interface.
B. Experimentation and Results
Developed system was evaluated with following experimental scenarios. The load devices for the scenarios arelighting, Air cooler (AC), Outlets1 and Outlets2. Outlets1
has an UPS and a desktop computer connected to the UPS
and Outlets2 has a laptop computer. Outlets1 and Outlets2
are given highest priority, where lighting is given the second
highest and finally the lowest priority to AC during power
outage. Output capacity of the local generator is assumed 2A.
Scenarios carried for experimentation are listed as below.
1)Power is provided from a power company and we canuse all devices. Whole current is around 2.3A.

Power outage on the power company is happened andpower is down for 30 seconds.
Local generator is turned on and power is resumed.
The output capacity is 2A.
After some seconds, we try to add new load, afilament bulb, to Outlets2.

Fig. 8 shows time series events of controlling algorithms
Simple and Simple + Adaptive respectively. Violet time seriesline shows the actual demand current from a load device andBlue time series line shows a threshold value for power outage
timing. Area covered with blue shows the first events of ourscenario, where area in red is power outage (the second event),
yellow shows the local generator time (the third event) andviolet shows the adding new load (the forth event). Power
capping value is shown in Fig. 9 and it is observed that bothalgorithms satisfy the specification of power capping to 2A. A
detailed discussion of this result is given in next subsection.
Two algorithms shows the same controlling actions until a
new device is added to the system. The power consumption
is over the threshold and Simple + Adaptive way reallocates
the threshold values from lower priority to higher priority andSimple way does not change the threshold values. With the
Simple controller, the threshold value is determined according
to the occurring of power outage and does not take careof changes in load during power outage. Therefore, whenresidents try to add new device to Outlets2, Outlets2 is tripped
even Outlets2 is at high priority. Simple + Adaptive controller
enables reallocation of threshold values based on the priorities
when some load devices requires more electricity. You can
see the reallocation when user insert new device to Outlets2
in violet area. Outlets2 has higher priority than Lighting. The
controller reallocate threshold value from Lighting and added
to Outlets2 which is shown in Fig. 8. In addition, after sometime period, when higher priority UPS is charged enough,the Master Controller allocates this residual threshold value
to Lighting shown at orange area in Fig. 8(4′). Eventually,
Simple + Adaptive algorithms enables four devices working
after the forth event. Simple + Adaptive algorithm allows larger
number of devices and improve utilization of local generator
far efficiently than Simple algorithm.
C. Discussions
We compared two algorithms and conventional circuit
breaker, assuming that threshold value of the breaker is statically 1A. Lighting and AC circuits should be turned off
because the output capacity of the generator is 2A. TABLE II
shows whole current flow on each events on our experimental
scenario. We use the same data on Simple + Adaptive toevaluate a case of conventional breaker. After the forth event,the utilization factors are 50%, 55% and 65% for conventional
static, Simple and Simple + Adaptive respectively. Moreover,
Simple + Adaptive algorithm achieves 90% utilization factor
after 1 minute. This is 40% higher than conventional static.
This result shows that our proposal system is far better thanconventional system.
As shown above, we show the proposed SCB system
works well, as we expected, with the established experimental
scenarios. However, the experimentation is only with four
SCBs in the prototype system. A full scale system with at

VI.

Fig. 8: Result of experimentation from 2 types of strategies:(a) Simple strategy, (b) Simple + Adaptive strategy; Thresholds
are reallocated from free area or low priority area to anotherin violet and orange area in (b).

In this paper, a novel on-the-premises micro grid systemusing Smart Circuit Breaker (SCB) with local power generator
is proposed so as to improve the utilization factor of the generator focusing on the operation during the power outage event.
The proposed system contributes to the improvement of operational efficiency and quality of service, even for the demand
response against peak power usage in the existing buildings.
We define the sub networks with SCB in the building, while
dynamically configuring the power capping threshold value forevery single SCB by the central controller in real-time fashion.
Experimental operation with Simple and Simple + Adaptivealgorithms shows that the proposed system works well and will
be able to improve the operational efficiency of local power
generator installed in the building. The system is evaluatedwith prototype implementation. The evaluation result, withthe best scenario, the proposed system achieved 40% higher
utilization factor compared to the conventional circuit breakersystem with the static threshold value setting.
R EFERENCES
[1]
[2]

[A]

Simple

[3]
[4]
[5]

Simple + Adaptive[6]

Blue: AC, Red: Lighting,Green: Outlet1 (UPS + Desktop) and Black: Outlet2 (Laptop + another)

Fig. 9: Result of power capping
least 30 SCBs controlling real time loads in each room of
the building is planned to confirm that the proposed system
works efficiently for the most of the scenarios/events. Wirelesscommunication networks which is backbone for our prototype
system is deployed with ZigBee. We may want to carry out thesame experiments using different communication technologies
like PLC (Power Line Communication), WiFi or Wi-SUN toevaluate the QoS when network scales is getting larger. Since
the SCB is connecting to power lines, the use of PLC would bealso a good option. WiFi environment in commercial building
is generally with battery backup and it works even in power
outage time. Investigation on available wireless technologies
and coming up with efficient and economical option is also
future scope of our research.
TABLE II: Comparison of current flow between conventional
static threshold, Simple and Simple + Adaptive; unit is A.
Event No12
3
4

Conventional breakers(threshold is static 1A)

SCBs
Simple

2.3
0
0.8
1.0

2.2
0
1.4
1.1

and

SCBs and Simple +Adaptive
2.3
0
1.55
1.3, 1.8 (after 1min)

C ONCLUSION

[7]
[8]

[9]
[10]

[11]

[12]

[13]

[14]

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