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Electric power transmission grid has been the backbone of the energy infrastructure for
many years and has been closely monitored via advanced supervisory control and data
acquisition systems (SCADA) managed by the regional and local control centers. On the
other hand, distribution systems, which are generally defined as those systems operating
at
We see two issues related to the above development:
1) Modeling
2) Monitoring and control
Modelling
1. Loads: Single, double and three phase loads couldexist at any bus. Three types of loads
can be specifiedand any combination of them may exist at each
bus:
1.a Constant Power: Real and reactive power injections at the bus are specified for each
phase.
1.b Constant Current: Current magnitude and itspower factor are specified for individual
phases at the bus. At each iteration, these valuesare converted into equivalent real and
reactivepower injections using the most recentlycalculated value of the bus voltage. Upon
convergence,the specified current and its associatedpower factor will be satisfied.
1.c Constant Impedance: These types of loads may be connected in delta (¢), Star (Y)
with floating neutral,Y with grounded neutral or Y with a neutral grounded through an
impedance, connection is equivalent to Ywith floating neutral and hence treated with in
the model of Figure 1. Admittances of the phase iload and the neutral-ground

2. connectionbranch are labelled by yi and y0 respectively in the figure 1. They are
modelled using a (3x3) admittance matrix Yload, which is added to the block diagonal
element of the
FIGURE 1. Constant impedance type load where we are having three phases with
grounded resistors.
Three phase bus admittance matrix of the system where each upper block represent a
phase and lower block is grounded resistor.
2. GENERATION
Remote generation at any of the systembuses should be modelled as a controlled
voltagesource whose total real power output is specified.Generator terminal voltage is
typically controlledvia the specification of the positive sequencecomponent only. This is
accomplished via the useof a three phase synchronous generator model withbalanced
excitation voltage.
3. Transformers:
Transformers with fixed or controllabletaps have to be modelled. They may be
threephase units or may be single phase units connectedin any number of the individual
phases dependingon the feeder configuration. Assuming that they operatein their linear
region and neglecting their excitation.
4. Shunt Capacitors/Reactors
Single or three phaseshunt elements directly connected between a bus and the ground
should be modelled. Three phase units can be connected in Y (grounded or with floating

3. neutral) or delta. They are modelled in the sameway as the constant impedance type loads
described.
5. Implementation of Constraints
1. Tap/Voltage Limits: Voltage regulators are represented as variable tap transformers
with controllable terminal voltages. This requires the adjustment of taps during the
iterative solution of the power flow equations, so that the specified bus voltages are
maintained. In the current implementation, this is accomplished by using the approximate
sensitivities of the taps to the bus voltages. However, this feature
can be easily modified to maintain any function of the system bus voltages, for instance
the difference between the secondary and primary, if needed. Details of automatic tap
adjustments will be presented in the next section.
2. Q-limits: Remote generators are modelled as three phase sources with specified total
real power output and positive sequence terminal voltage. Reactive power output Q, of
the generator is adjusted to maintain the specified voltage while respecting
the limits on Q. At each iteration, these limits are checked and if exceeded, the bus type is
switched back to PQ where the exceeded limit is used as the specified Q for this PQ bus.
6 Solution Adjustments
1. Voltage Regulators:
Three phase transformers with variable taps, are used to represent the voltage regulators.
Either the secondary voltage or any other bus voltage along the feeder can be specified as
the controlled bus voltage by the regulating tap. The error feedback method that is
successfully utilized in single phase power flow solutions is employed for tap
adjustments.
7. Remote Generator Q-limits:
The experience with the single phase power flow solutions implies that the Q-limit
adjustments should be attempted after the solution is sufficiently converged. Hence, a
threshold is used on the power mismatches so that premature Q-limit and bus type
adjustments can be avoided. Once the violated limits on Q generation are detected, then
the corresponding bus type is converted to PQ and the injected Q is fixed at the limit
value. Similarly inverse procedure is used to backup previously converted buses whose
voltages get corrected during subsequent iterations.
8. POWER DISTRIBUTION
In an AC power system, it is well known that reactive power flows impact the voltage
of the system buses.Thus, by controlling reactive power it is possible to also provide
voltage control, which is extremely important to ensure voltage stability of the system.
Reactive power control can be achieved by controlling the excitation of certain generators
in the system, by switching in and out banks of capacitors connected to certain buses of
the grid, or by changing the impedance of certain transmission lines using FACTS
devices

4. On the distribution side of a power system, it has been acknowledged that there exist
many distributed energy resources (DERs) that can be potentially used to provide reactive
power control. include power electronics-interfaced solar installations in residential and
commercial building roofs, and motor drives.
While the primary function of these power electronics-based systems is to control active
power flow, when properly controlled, they can also be used to provide reactive power
support for voltage control in the grid they are connected to. In this regard, a sensitivity-
based method is proposed in to identify buses in a power network that can be employed
to adjust the voltage profile of the network by controlling the reactive power injection in
those buses.
However, it does not address the control strategy needed to coordinate the DERs
connected to a particular bus so as to obtain the reactive power injection that is required
at that bus. This research addresses this problem by developing control and coordination
strategies that can be used by DERs to determine the specific amount of reactive power
they need to provide.
9 Structure and how it works
A solution to the above problem can be achieved through a centralized control strategy
where each DER in the distribution network (connected to a certain bus of the power
system) is commanded from a central controller located, for example, at the substation
that interconnects the distribution network and the transmission/subtransmission network.
This central controller issues a command to each DER so that collectively the resources
account for the necessary amount of reactive power demanded by the central controller,
providing reactive power support to the sub-transmission/transmission grid, much like a
bank of capacitors would provide.
However, to achieve this goal in this centralized fashion, it is necessary to overlay a
communication network connecting the central controller with each DER.
An alternative approach to provide reactive power support, utilizing distributed
control/coordination strategies, which offer several advantages, including the following:
i) distributed control and coordination strategies are more economical because
they do not require communication between a centralized controller and the
various devices.
ii) ii) they do not require complete knowledge of the DERs available.
iii) iii) they can be more resilient to faults and/or unpredictablebehavioral patterns
by the DERs. The proposed approaches rely on a distributed control strategy
whereeach DER can exchange information with a number of other “close-by”
resources, and make a local control decision based on this available
information. Collectively, local control decisions made by the resources
should have the same effect as the centralized control strategy. Such a solution
could rely on inexpensive and simple communication protocols, e.g., ZigBee
technology that would provide the required local exchange of information for
the distributed control approach to work.

5. In the setup, the DERs can be thought of as nodes in a network, where each
node can exchange information with neighboring nodes such that, through an iterative
process, each DER in the network is able to compute the amount of reactive power that it
needs to provide, such that the resources collectively account for the predetermined
(requested) amount of reactive power. We will investigate algorithms that solve this
coordination/cooperation problem and experimentally demonstrate their feasibility by
implementing them in a hardware platform using ZigBee technology and the Arduino
prototyping platform.
10 Advantages
We believe that the proposed distributed control strategies for utilization of reactive
power resources address two features identified as key to achieving the Smart Grid vision
• 1.They enable the active participation of consumers via demand response. In this
regard, consumers have the choice to enable resources, such as solar installations
in buildings and PHEVs, to provide reactive (and active) power support, for
which they can be paid for by the corresponding utility.
• 2. It allows asset optimization and efficient operation. In this regard, even when
banks of switched capacitors or other existing means that provide reactive power
control cannot be completely replaced by DERs, it is possible to reduce their size.
• 3.By generating reactive power closer to the points where it is consumed, the
losses in the transmission and distribution systems can be reduced.
11 Risk Assessment for Transformer Loading
In recent times due to competiton the electric energy market environment, the incentive
to heavily load power transformers is being driven by the need to achieve increased
profits and the related reluctance to invest in new facilities. Hence, there is considerable
interest in identifying decision-making criteria so that they can be fully, but safely
utilized
1.The issue is addresed by describing a method for computing risk as a function
of transformer loading. The computed risk can be used to identify individual transformer
loading limits. It can also be used, together with risk calculation for transmission line
A. Overload,
B.Voltage collapse
C. Voltage out-of-limit
D. Transient instability
to obtain a composite risk as a function of operating
conditions .
The condition that limits the transformer loading capabilities is
1. Temperature of the winding
2. The insulation . This condition is characterized by the winding hottest-spot
temperature (HST).

6. 12 What is HST ?
The winding HST in the top or in the center of the high or low voltage winding is the
worst (highest) temperature for which the transformer insulation system is subjected.
13 HST Depends on what ?
HST Is a function of
A. Ambient temperature
B. Load shape
C. Transformer characteristics.
14 Cause and effects
1) Higher winding HST causes degradation in the strength of the winding insulation
material.
2) High temperatures decrease the mechanical strength and increase the brittleness
of fibrous insulation, increasing the potential for transformer failure.
3) Most of these studies focus on improving HST calculation models or developing
methods for assessing the influence of transformer thermal delays on short term
high loading.
15 Hottest-Spot Temperature Model
Transformer insulation deteriorates as a function of time and temperature. Since the
temperature distribution in most transformers is not uniform, the most common practice
is to consider the aging effects produced by the winding hottest-spot temperature.
16 Uncertainties in Hottest-Spot Temperature Model
When using the preceding model to calculate the transformer HST, there is typically
some uncertainty regarding loading and ambient temperature. In the following, we
provide probabilistic models to describe these uncertainties.
• Probabilistic Transformer Loading Profiles: Transformer daily load patterns in the
future can be obtained by load forecasting, but load forecasting always has errors, and
this error can be significant in today’s deregulated environment. We assume that this
uncertainty can be described by a normal distribution with the forecasted value as its
Mean.
• Distribution of Ambient Temperature:
Similarly, for temperature uncertainty caused by weather forecasting error,
we assume that it can also be described by a normal distribution
with the forecasted value as its mean.

7. • Correlation between Loading Profiles and Ambient TemperatureProfiles
The loading profiles are correlated with ambient temperature profiles. Like For example
during winter peak loads usually occur on the coldest days of the year on the other hand
in summer peak loads occur on the hottest days of the year. So in winter the correlation
between load and temperature should be negative; in summer it should be positive.
17 Risk Calculation
I. Risk has been defined as the product of probability and impact . The total impact
of transformer thermal overload includes both the impact of loss of life and
failure.
II. It is baased on conditional proability.
18 TRANSFORMER REFERENCE RISK LEVELS
Power system load is typically cyclic in nature with both daily and annual cycles. For the
daily cycle, it is usually assumed that transformers operate on a load cycle that repeats
every 24 hours. The load cycle changes with seasons. It is usually appropriate to assume
that the duration of the same load cycle extends over 90 days.
19 Reference Loading Levels
• Normal Life Expectancy Loading
Normal life expectancy loading (NLEL) is defined as
Loading for which the winding HST and maximum top oil temperatures as permitted in
IEEE C57.12.00-1987 are not exceeded, although the loading may exceed nameplate
rating. This loading can be continued indefinitely; it is considered to be risk-free to
remain in NLEL. To remain in NLEL, it is suggested that the winding should be HST be
kept in the range of 110 C–120 C.
• Planned Loading Beyond Nameplate Rating
Planned loading beyond nameplate rating (PLBNR) is is defined as
Loading for which the winding HST or top oil temperature exceeds the levels suggested
for NLEL. It is accepted by the user as an anticipated, normal, reoccurring loading.
This loading is allowed with all components in service, yet some risk is associated with.
To remain in PLBNR, it is suggested that operation not exceed 4 hours per day
when the winding HST is in the range of 120 C–130 C.
• Long-Time Emergency Loading
Long-time emergency loading (LTEL) is is defined as
Loading for which the winding HST or top oil temperature exceeds those permitted for
rated load operation. It is usually allowed only under conditions of prolonged outage of
some system elements.

8. To remain in LTEL, it is suggested one 24-hour period contains no more than six hours
operation when the winding HST is in the range of 130 C–140 C, together with no more
than four hours operation when the winding HST is in the range of 120 C–130 C.
• Short-Time Emergency Loading
Short-time emergency loading (STEL) is defined as
Loading for which the winding HST or top oil temperature exceeds the limits given for
PLBNR. It is an unusually severe condition typically acceptable only after the occurrence
of one or more unlikely events that seriously disturb normal system loading.
20 CONCLUSION
We provide a risk-based assessment method of transformer thermal loading capability.
Compared with the traditional deterministic methods has the following advantages.
• It determines a realistic estimate of transformer thermal loading capability by using
probabilistic characterization of uncertainty rather than using conservative deterministic
values.
• It provides a quantitative risk index that can be used to detect high risk situations.
• It can also be used, together with risk calculation
I. Transmission
II. Lines overload
III. Voltage collapse
IV. Voltage out-of limit
V. Transient instability to obtain a composite risk as a function of operating
conditions.
The risk calculation method is helpful in making decisions related to balancing risk
against the economic benefits that may result from a transformer loading level.