V. M. Istemihan Genc

A Real-Time Self-Healing Methodology Using Model and Measurement Based Islanding Algorithms

In this paper, a new real-time defensive islanding method, which is adaptive to the operating conditions is proposed. In the method, a number of candidate islanding schemes are generated using both model- and measurement-based islanding algorithms after detecting a severe fault in the system by means of a new severity index based on generator bus voltage frequency measurements. Of model-based algorithms, slow coherency-based islanding, in which the prefault measurements are utilized, is adopted. On the other hand,

Artificial neural network based algorithm for early prediction of transient stability using wide area measurements

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A security assessment for slow coherency based defensive islanding

-means, hierarchical, and fuzzy relational eigenvector centrality-based clustering are employed as measurement-based islanding algorithms, where the postfault measurements of the evolving dynamics after the severe fault are utilized. A faster-than-real-time software platform is, then, employed to validate the success of the candidate schemes in healing the system. Among the successful schemes, the one resulting in the least load imbalance is chosen to be applied. All the computations from the detection of the fault to the application of islanding are performed in real-time, directly after the occurrence of the fault. The proposed method is demonstrated on the 37-generator 127-bus WSCC power test system, and on a model of the Turkish power system to assess the method’s performance.

Adapting the defensive islanding for arming the power system under changing operating conditions

Early prediction of the transient stability of power systems after fault occurrences has a great impact on the performance of wide area protection and control systems designed against transient instabilities. In this paper, an artificial neural networks based methodology is proposed for predicting the power system stability directly after clearing the fault. A dataset is generated to train a multilayer perceptron off-line, which is then used for early online prediction of any transient instability. The neural network is fed by the inputs, which are the pre-fault, during-fault, and post-fault voltage magnitude measurements collected from the phasor measurement units. The success and the effectiveness of the proposed method are demonstrated, as it is applied to the 37-generator 127-bus power test system and an accuracy above 99% is obtained in the early prediction of transient instabilities.

An optimization method for preventive control using differential evolution with consecutive search space reduction

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas as soon as possible to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The design of the defensive islanding must address the questions of where and when to island the system to ensure minimum impact and the recovery of the affected portion of the system. In this paper, the critical islanding time is explored as a security index for applying the defensive islanding where the boundaries of the islands are decided by using the slow coherency concept between generators. The computations are performed on a test system to specify the maximum allowed time for operators to apply islanding before falling in blackout due to some critical contingencies.

K-means and fuzzy relational eigenvector centrality-based clustering algorithms for defensive islanding

Defensive islanding is one of the corrective control methods for securing the power system from severe cascading contingencies. Since the power system is operating in continuously changing operating conditions, the defensive islanding scheme must be adapting to these changes to arm the system against any potential instability. In this paper, we explore the effectiveness of a defensive islanding method to adapt to these changes by periodically updating the islanding scheme based on wide-area measurements. The proposed method uses the bus voltage angles measured by phasor measurement units to determine the islands via clustering algorithms. Among them, K-means clustering algorithm is adopted and explored as its performance during the course of the islanding scheme is studied. The proposed method is demonstrated on a 16-generator 68-bus power system and its performance is discussed.

Multi-objective optimization for voltage regulation in distribution systems with distributed generators

This paper proposes a new methodology to improve the population based optimization techniques applied for preventive control actions enhancing power system security. The preventive control studied includes both generation rescheduling and load curtailment. We first investigate how the size of the search space affects and improves the best solution obtained in the optimization process. Then, we develop a new methodology that involves a number of optimization algorithms running consecutively as the size of the search space of each algorithm is reduced according to the objective function. The extensive computational requirement for dynamic security assessment during the optimization processes is overcome by the application of neural networks. The methodology is successfully applied for solving the security constrained optimization problem of a 16-generator 68-bus test system with both continuous and discrete decision variables using consecutive differential evolution optimization algorithms.

A coherency based generation rescheduling against multiple contingencies

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the defensive islanding. In this paper, two partitioning methods are proposed, K-means clustering algorithm and fuzzy relational eigenvector centrality-based clustering algorithm. The proposed methods are using the data measured by phasor measurement units to determine the islands to be used in the defensive islanding. The proposed methods are demonstrated on the 16-generator 68-bus power system and their performances are discussed as their results are compared.

A new dynamic security enhancement method via genetic algorithms integrated with neural network based tools

In this study, in order to reduce the abnormal voltage drop or rise in the distribution systems with distributed generators, we consider a multi-objective function to be optimized in such a way that the voltage profile is enhanced while the total active power loss in the network is minimized under some operational constraints. Capacitors and static VAR compensators are also coordinated with the distributed generators in the network to acquire a better enhancement in the voltage profile. The purpose of this study is to obtain the optimum sizes and locations of these system components so that the active power losses and the deviations in bus voltages from their nominal values are minimized. The problem is formulated as a multi-objective optimization problem and solved by the goal attainment method. The studies are demonstrated on an IEEE 34-bus test system where the direct load flow method is utilized for load flow calculations.

Preventive and corrective control applications in power systems via Big Bang–Big Crunch optimization

In this paper, a preventive control method against transient instabilities due to a set of critical contingencies that could occur in a large power system is proposed. The generation rescheduling, adopted as the preventive control, is based on the coherency between the generators. The rescheduling is done by attempting to bring the generators’ rotor speeds equal after a three phase fault that might cause instability. The proposed methodology involves off-line simulations to determine the system response and to check the severity of each contingency. For each critical contingency, a scaling factor is assigned to scale the speed trajectory of the contingency. Active-set sequential quadratic programming (SQP) is used to optimize the scaling factors in such a way that the rescheduling method based on the scaling factors improves the dynamic security and restores the system’s stability for all contingencies that are taken into account.