Shahbaz A. Siddiqui

Identification and reduction of impact of islanding using hybrid method with Distributed Generation

In this paper a hybrid method is proposed to reduce the impact of islanding in the presence of Distributed Generation (DG). The issue of islanding is critical in the distribution system as the non- detection of islanding may lead to the collapse of the system. The islanding is prominent due to the increased penetration levels of DG in the network. In the proposed method, first the placement of DG is performed using a two stage Genetic Algorithm (GA), by dividing the system into zones, to improve the overall voltage profile and to reduce the active power losses. The two stage optimization ensures a faster convergence of results and better quality of solutions. The bus vulnerable for islanding is identified using a hybrid approach combining the existing active and passive methods islanding. The proposed scheme is tested on IEEE 33 and 69 bus radial systems and the results obtained show the effectiveness of the method.

Preventive and Emergency Control of Power System for Transient Stability Enhancement

This paper presents preventive and emergency control measures for on line transient stability (security) enhancement. For insecure operating state, generation rescheduling based on a real power generation shift factor (RPGSF) is proposed as a preventive control measure to bring the system back to secure operating state. For emergency operating state, two emergency control strategies namely generator shedding and load shedding have been developed. The proposed emergency control strategies are based on voltage magnitudes and rotor trajectories data available through Phasor Measurement Units (PMUs) installed in the systems. The effectiveness of the proposed approach has been investigated on IEEE-39 bus test system under different contingency and fault conditions and application results are presented.

Real-Time Monitoring of Post-Fault Scenario for Determining Generator Coherency and Transient Stability Through ANN

Power system monitoring and control in real time is a challenging task for modern power system due to large operational constraints. The deployment of phasor measurement units (PMUs) at key locations provides an opportunity for devising effective power system monitoring and control measures. In this study, a new method is proposed to determine the real-time transient stability status and identification of the coherent generator groups by predicting the rotor angle values following a large disturbance through radial basis function neural network. The first six cycles of synchronously sampled post-fault data measurements from PMUs consisting of rotor angles and voltages of generators are taken as the input to the neural network to predict the future state of the system. The proposed method can also determine the synchronism state of the individual machine in real time. The proposed scheme is demonstrated on the IEEE-39 bus test system at different operating conditions.

Artificial neural network based early detection of real-time transient instability for initiation of emergency control through wide-area synchrophasor measurements

This paper proposes an approach for early detection of transient instability of power system for initiating the emergency control in time. The synchrophasor measurements are used for real-time monitoring of the system. The Artificial Neural Network (ANN) is used as classifier for predicting the transient instability status of the system with rotor angles and speeds (frequency) of generator as inputs at different consecutive cycle lengths after fault clearing. The stability status obtained from ANN can be utilized for initiating the emergency control actions within few cycles from fault clearing. The proposed scheme is able to successfully predict the transient stability status of the system for unseen operating conditions with varying topology. The proposed method is investigated on IEEE-39 New England system for its real-time applications and results obtained reflect the effectiveness of the proposed methodology.

An improved hybrid method to reduce the effect of islanding in the presence of optimally located DGs

The islanding issue in the distribution system poses great threat to the system during operation. With increased penetration levels of DG, the system is more vulnerable to islanding, leading to the collapse of the system. This paper proposes an improved method to detect the islanding in the system with DG penetration. The proposed work is a two-step method, firstly the siting and sizing of DG is obtained by two stage Genetic Algorithm (GA) to improve the voltage profile and to reduce the active power losses. A two stage GA ensures a faster convergence and better quality of solutions. Finally the proposed improved hybrid method is applied to detect the bus most susceptible for islanding under varying operating conditions. The improved hybrid islanding detection method utilizes the positive features of existing active and passive methods simultaneously. The proposed work is tested on IEEE 33 and 69 bus radial systems and the results obtained are promising.

Real-time identification of generator coherent groups through synchrophasor measurements and ANN

Power system monitoring and control in real-time is a challenging task for modern power system due to large number of operational constraints involved. This paper proposes a method to find the real-time transient stability state and identification of the coherent generator groups by predicting the rotor angle values following a large disturbance through radial basis function neural network. The first six cycle data of rotor angles and voltages of generators from fault clearing obtained through synchrophasor measurements are taken as the input to the neural network. The proposed method is also able to determine synchronism state of the individual machine in real-time. The proposed scheme is investigated on IEEE-39 bus test system to show the effectiveness of the proposed scheme.

An Improved Islanding Detection Technique and priority based load shedding for distribution system with multiple DGs

The identification and operation of islands in the presence of Distributed Generation (DG) Units has become challenging. The non-detection of islanding could lead to a cascaded failure of the system. This paper proposed an Improved Islanding Detection Technique (IIDT) for early and accurate identification of the bus for islanding in the presence of multiple DG units. In the islands identified thus, a priority based load shedding scheme is proposed to alleviate the power mismatch with minimum amount of load shedding. The proposed schemes have been tested on standard IEEE 33 and 69 Bus test systems and the results obtained are promising.

Voltage stability improvement of distribution networks by enhancing DGs impacts

Integration of Distributed Generation (DG) in electric power networks poses complexities and challenges to the system operator. The DGs can provide positive impact on the operation of the system if located and cited properly. This paper proposes the DG (Type I and Type III) placement and sizing algorithm to reduce system losses and improve the voltage profile of the system. Modal analysis and Continuous Power Flow (CPF) are used to determine the candidate buses for the DG placement. A voltage stability constrained optimal sizing of DG is obtained using Genetic Algorithm (GA). The superiority of the proposed algorithm has been verified by the various performance indices. The performance of the proposed methodology is investigated on standard IEEE-33 and IEEE-69 bus radial distribution system.

Optimal Placement of Distributed Generators in Radial Distribution System for Reducing the Effect of Islanding

The present trend of increasing the penetration levels of Distributed Generator (DG) in the distribution network has made the issue of Islanding crucial for the reliable operation of the network. The islanding, if not detected early may lead to the collapse of the system as it can drive the distribution system to the cascaded failure. In this paper, an extensive study of the effect of DG placement and sizing is performed by dividing the system into different zones to obtain a reduced effect of islanding. The siting and sizing of DG is carried out to improve the overall voltage profile or/and reduction in active power loss using two stage Genetic Algorithm (GA). In the first stage a basic knockout selection is considered and the best population is taken for next stage, where roulette selection for crossover and mutation is performed for optimal placement and sizing of DGs. The effect of the islanding, due to load variations is reduced by optimal siting and sizing of DG. The effectiveness of the proposed scheme is tested on the IEEE 33 and 69 radial bus systems and the results obtained are promising.

Optimal voltage regulation of a distribution network by output power management of DGs

The integration of Distributed Generation (DG) into distribution networks is associated with large practical problems. With the large variation in load demand and the high penetration level of these distributed energy resources, the voltage stability issue has become a prime concern for the system operator. This paper proposes an optimal voltage control method for regulating the voltage of LV distribution network with DGs whenever the voltage at any bus exceeds normal operating limits. The algorithm is based on the voltage sensitivities to active and reactive power. The voltage regulation is achieved by power output management of DGs with re-dispatching their power outputs using Nonlinear Programming (NLP) optimization technique. The algorithm is tested on standard IEEE-33 bus radial distribution system and the result obtained shows the effectiveness of the proposed algorithm.