Abstract: This paper presents state estimation with Phasor Measurement Unit (PMU) allocation to obtain complete observability of network. A matrix is designed with modeling of zero injection constraints to minimize PMU allocations. State estimation algorithm is developed with optimal allocation of PMUs to find accurate states of network. The incorporation of PMU into traditional state estimation process improves accuracy and computational performance for large power systems. The nonlinearity integrated with zero injection (ZI) constraints is remodeled to linear frame to optimize number of PMUs. The problem of optimal PMU allocation is regarded with modeling of ZI constraints, PMU loss or line outage, cost factor and redundant measurements. The proposed state estimation with optimal PMU allocation has been compared with traditional state estimation process to show its importance. MATLAB programming on IEEE 14, 30, 57, and 118 bus networks is implemented out by Binary Integer Programming (BIP) method and compared with other methods to show its effectiveness.
Abstract: Power system security analysis is the most time demanding process due to large number of possible contingencies that need to be analyzed. In a power system, any contingency resulting in security violation such as line overload or low voltage may occur for a number of reasons at any time. To efficiently rank a contingency, both probability and the extent of security violation must be considered so as not to underestimate the risk associated with the contingency. This paper proposed a contingency ranking method that take into account the probabilistic nature of power system and the severity of contingency by using a newly developed method based on risk factor. The proposed technique is implemented on IEEE 24-bus system.
Abstract: At present, the evaluation of voltage stability
assessment experiences sizeable anxiety in the safe operation of
power systems. This is due to the complications of a strain power
system. With the snowballing of power demand by the consumers
and also the restricted amount of power sources, therefore, the system
has to perform at its maximum proficiency. Consequently, the
noteworthy to discover the maximum ability boundary prior to
voltage collapse should be undertaken. A preliminary warning can be
perceived to evade the interruption of power system’s capacity. The
effectiveness of line voltage stability indices (LVSI) is differentiated
in this paper. The main purpose of the indices used is to predict the
proximity of voltage instability of the electric power system. On the
other hand, the indices are also able to decide the weakest load buses
which are close to voltage collapse in the power system. The line
stability indices are assessed using the IEEE 14 bus test system to
validate its practicability. Results demonstrated that the implemented
indices are practically relevant in predicting the manifestation of
voltage collapse in the system. Therefore, essential actions can be
taken to dodge the incident from arising.
Abstract: Uncertainty of system operating conditions is one of the causative reasons which may render to the instability of a transmission system. For that reason, accurate assessment of transmission reliability margin (TRM) is essential to ensure effective power transfer between areas during the occurrence of system uncertainties. The power transfer is also called as the available transfer capability (ATC) which is the information required by the utilities and marketers to instigate selling and buying the electric energy. This paper proposes a computationally effective approach to estimate TRM and ATC by considering the uncertainties of system cascading collapse and transmission line outages. In accordance to the results that have been obtained, the proposed method is essential for the transmission providers which could help the power marketers and planning sectors in the operation and reserving transmission services based on the ATC calculated.
Abstract: This paper aims to select the optimal location and
setting parameters of TCSC (Thyristor Controlled Series
Compensator) controller using Particle Swarm Optimization (PSO)
and Genetic Algorithm (GA) to mitigate small signal oscillations in a
multimachine power system. Though Power System Stabilizers
(PSSs) are prime choice in this issue, installation of FACTS device
has been suggested here in order to achieve appreciable damping of
system oscillations. However, performance of any FACTS devices
highly depends upon its parameters and suitable location in the
power network. In this paper PSO as well as GA based techniques are
used separately and compared their performances to investigate this
problem. The results of small signal stability analysis have been
represented employing eigenvalue as well as time domain response in
face of two common power system disturbances e.g., varying load
and transmission line outage. It has been revealed that the PSO based
TCSC controller is more effective than GA based controller even
during critical loading condition.
Abstract: The demand of the energy management systems (EMS) set forth by modern power systems requires fast energy management systems. Contingency analysis is among the functions in EMS which is time consuming. In order to handle this limitation, this paper introduces agent based technology in the contingency analysis. The main function of agents is to speed up the performance. Negotiations process in decision making is explained and the issue set forth is the minimization of the operating costs. The IEEE 14 bus system and its line outage have been used in the research and simulation results are presented.
Abstract: Power flow (PF) study, which is performed to
determine the power system static states (voltage magnitudes and
voltage angles) at each bus to find the steady state operating
condition of a system, is very important and is the most frequently
carried out study by power utilities for power system planning,
operation and control. In this paper, a counterpropagation neural
network (CPNN) is proposed to solve power flow problem under
different loading/contingency conditions for computing bus voltage
magnitudes and angles of the power system. The counterpropagation
network uses a different mapping strategy namely
counterpropagation and provides a practical approach for
implementing a pattern mapping task, since learning is fast in this
network. The composition of the input variables for the proposed
neural network has been selected to emulate the solution process of a
conventional power flow program. The effectiveness of the proposed
CPNN based approach for solving power flow is demonstrated by
computation of bus voltage magnitudes and voltage angles for
different loading conditions and single line-outage contingencies in
IEEE 14-bus system.
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.