Abstract: Monocopter is a single-wing rotary flying vehicle
which has the capability of hovering. This flying vehicle includes two
dynamic parts in which more efficiency can be expected rather than
other Micro UAVs due to the extended area of wing compared to its
fuselage. Low cost and simple mechanism in comparison to other
vehicles such as helicopter are the most important specifications of
this flying vehicle.
In the previous paper we discussed the introduction of the final
system but in this paper, the experimental design process of
Monocopter and its control algorithm has been investigated in
general. Also the editorial bugs in the previous article have been
corrected and some translational ambiguities have been resolved.
Initially by constructing several prototypes and carrying out many
flight tests the main design parameters of this air vehicle were
obtained by experimental measurements. Eventually the required
main monocopter for this project was constructed. After construction
of the monocopter in order to design, implementation and testing of
control algorithms first a simple optic system used for determining
the heading angle. After doing numerous tests on Test Stand, the
control algorithm designed and timing of applying control inputs
adjusted. Then other control parameters of system were tuned in
flight tests. Eventually the final control system designed and
implemented using the AHRS sensor and the final operational tests
performed successfully.
Abstract: Wind farms (WFs) with high level of penetration are
being established in power systems worldwide more rapidly than
other renewable resources. The Independent System Operator (ISO),
as a policy maker, should propose appropriate places for WF
installation in order to maximize the benefits for the investors. There
is also a possibility of congestion relief using the new installation of
WFs which should be taken into account by the ISO when proposing
the locations for WF installation. In this context, efficient wind farm
(WF) placement method is proposed in order to reduce burdens on
congested lines. Since the wind speed is a random variable and load
forecasts also contain uncertainties, probabilistic approaches are used
for this type of study. AC probabilistic optimal power flow (P-OPF)
is formulated and solved using Monte Carlo Simulations (MCS). In
order to reduce computation time, point estimate methods (PEM) are
introduced as efficient alternative for time-demanding MCS.
Subsequently, WF optimal placement is determined using generation
shift distribution factors (GSDF) considering a new parameter
entitled, wind availability factor (WAF). In order to obtain more
realistic results, N-1 contingency analysis is employed to find the
optimal size of WF, by means of line outage distribution factors
(LODF). The IEEE 30-bus test system is used to show and compare
the accuracy of proposed methodology.