Rotor angle transient stability methodologies of power systems: a comparison

Today's power system grids are massively interconnected with newer technologies and control devices integrated into the system all for enhanced efficiency and economic benefits in operation. This property makes the grid highly dynamic and diverse thus, concluding it as the biggest artificial living network on earth. To observe the power system security requirements, planning and safe operation of today's system becomes imperatively necessary. If the power system security criterion is ignored, then power blackouts will be born and the system response will distribute severe socio-economic impact, therefore, it is essential to know the dynamic security assessment (DSA) for the systems and allow it to remain in a stable state. This paper will execute a comparison of each method of transient stability index (TSI) and transient stability assessment (TSA) and how the classes of these methods are implemented. The classifications will highlight the difference between the methods and show the individual benefits and setbacks when applied on a practical large sized power system. This review paper can deliver an in-depth knowledge into the development of a rapid and reliable rotor angle transient instabilities technique towards improving DSA of a practical large-sized power system

Area-based COI-referred transient stability index for large-scale power system

This paper presents a new transient stability index called the Area-based COI-referred Transient Stability Index for a large electrical power system. A large power system is divided into smaller areas depending on the coherency of the system due disturbances before the index is applied on the system. The proposed index is defined by associating with each area of the power system an equivalent inertia representing the total inertia of the generation located in that area. Assuming that each area is coherent, it is possible to assimilate its behavior to that of a single large machine with same inertia and generation. It also offers a direct means of deriving the centre of inertia (COI). The COI provides very useful information for tracking the stability of interconnected areas. So, instead of assessing all generators’ rotor angles. Simulations on the large practical power system show the effectiveness of the proposed index

Transient stability emergency control of power systems employing UFLS combined with generator tripping method

This paper concerns with transient stability control which is part of transient stability assessment which needs to be considered so that the power systems remained intact when failures originating from faults occurred in power systems. Conventional UFLS system is designed to retrieve the balance of generation and consumption following disturbances occurrences in the system. In UFLS method, whenever the system's frequency drops below a predetermined value, the system loads are shed in stages. An efficient UFLS method needs to be devised so as to reduce the impacts of transient disturbance on power systems and prevent total system blackout. In this paper, an emergency control scheme known as the combined UFLS and generator tripping is developed in order to stabilize the system when unstable faults occurred in a power system. The performance of the combined UFLS and generator tripping scheme is compared with the conventional UFLS control scheme. The results show that the combined control scheme performed better

Transient stability emergency control using generator tripping based on tracking area-based rotor angle combined with UFLS.

This paper concerns with transient stability control which is part of transient stability assessment which needs to be considered so that the power systems remained intact when failures originating from faults occurred in power systems. Conventional under frequency load shedding (UFLS) system is designed to retrieve the balance of generation and consumption following disturbances occurrences in the system. In UFLS method, whenever the system's frequency drops below a predetermined value, the system loads are shed in stages. An efficient UFLS method needs to be devised so as to reduce the impacts of transient disturbance on power systems and prevent total system blackout. In this paper, an emergency control scheme known as the combined UFLS and generator tripping is developed in order to stabilize the system when unstable faults occurred in a power system. The performance of the combined UFLS and generator tripping scheme is compared with the conventional UFLS control scheme. The results show that the combined control scheme performed better

A new method of transient stability assessment in power systems using LS-SVM

This paper presents transient stability assessment of electrical power system using least squares support vector machine (LS-SVM) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9- bus test system considering three phase faults on the system. The data collected from the time domain simulations are then used as inputs to the LS-SVM in which LS-SVM is used as a classifier to determine the stability state of a power system. Principle component analysis is applied to extract useful input features to the LS-SVM so that training time of the LS-SVM can be reduced. To verify the effectiveness of the proposed LS-SVM method, its performance is compared with the multi layer perceptron neural network. Results show that the LS-SVM gives faster and more accurate transient stability assessment compared to the multi layer perceptron neural network in terms of classification results

Classification-based fast transient stability assessment of power systems using LS-SVM with enhanced feature reduction techniques

This paper presents fast transient stability assessment of a large 87-bus Malaysia test system using a new method called the least squares support vector machine (LS-SVM) with incorporation of feature reduction techniques. The investigated power system is divided into smaller areas depending on the coherency of the areas when subjected to disturbances. By doing this, the amount of data sets collected for the respective areas is reduced. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected are then used as inputs to the LS-SVM. The developed LS-SVM is used as a classifier to determine whether the power system is stable or unstable. The performance of the LS-SVM is enhanced by employing feature reduction techniques to reduce the number of features. It can be concluded that the LS-SVM with the incorporation of feature reduction techniques reduces the time taken to train the LS-SVM and improved the accuracy of the classification results

An improved method in transient stability assessment of a power system using probabilistic neural network

This paper presents transient stability assessment of electrical power system using probabilistic neural network (PNN) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on the system. The data collected from the time domain simulations are then used as inputs to the PNN in which PNN is used as a classifier to determine whether the power system is stable or unstable. To verify the effectiveness of the proposed PNN method, it is compared with the multi layer perceptron neural network. Results show that the PNN gives faster and more accurate transient stability assessment compared to the multi layer perceptron neural network in terms of classification results

Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques

This paper presents transient stability assessment of a large 87-bus system using a new method called the probabilistic neural network (PNN) with incorporation of feature selection and extraction methods. The investigated power system is divided into smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN. Feature reduction techniques are then incorporated to reduce the number of features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with the incorporation of feature reduction techniques reduces the time taken to train the PNN without affecting the accuracy of the classification results

Differential evolution optimization algorithm based on generation systems reliability assessment integrated with wind energy

Generating systems are said to be adequately reliable when they can satisfy the load demand. Meanwhile, the reliability of electrical systems is currently being influenced by the increasing acceptance of "Wind Energy Conversion System" (WECS) in power systems compared to other conventional sources. This stuffy proposed a novel optimization method labeled the "Differential Evolution Optimization Algorithm" (DEOA) to assess the reliability of power generation systems (PGS). The DEOA technique is used to improve the assessment of the reliability and adequacy of the generation systems by incorporating wind energy from a WECS. The basis of DEOA is the meta- heuristic searching used to simulate the generation systems operation and considering the random failures of existing systems and the unstable character of WECS- sourced wind energy. The effectiveness of the suggested algorithm to assess the reliability and adequacy of power generation systems with WECS was demonstrated. Additionally, the efficiency of the planned algorithm in numerical simulation was compared to that of the "Monte Carlo simulation" (MCS)

Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV

With the increase in power demand and limited power sources has caused the system to operate at its maximum capacity. Therefore, the ability of determine voltage stability before voltage collapse has received a great attention due to the complexity of power system. In this paper a prediction of voltage stability index (VSI) based on radial basis function neural network (RBFNN) for the Iraqi Super Grid network, 400KV. Learning data has been obtained for various settings of load variables using load flow and conventional FVSI method. The input data was performed by using a 135 samples test with different bus voltage (Vb), Bus active and reactive power (Pb, Qb), bus load angle (?b) and FVSIij. The RBFNN model has four input representing the (Vb, Pb, Qb and ?b), sixteen nodes at hidden layer and one output node representing FVSIij have been used to assess the security on line. The proposed method has been tested in the IEEE 30 and a practical system. In Simulation results show that the proposed method is more suitable for on-line voltage stability assessment in term of automatically detection of critical transmission line when additional real or reactive loads are added