(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

A review on battery management system from the modeling efforts to its multiapplication and integration

Fast and accurate contingency selection and ranking method has become a key issue to ensure the secure operation of power systems. In this paper multi-layer feed forward artificial neural network (MLFFN) and radial basis function network (RBFN) are proposed to implement the online module for power system static security assessment. The security classification, contingency selection and ranking are done based on the composite security index which is capable of accurately differentiating the secure and non-secure cases. For each contingency case as well as for base case condition, the composite security index is computed using the full Newton Raphson load flow analysis. The proposed artificial neural network (ANN) models take loading condition and the probable contingencies as the input and assess the system security by screening the credible contingencies and ranking them in the order of severity based on composite security index. The numerical results of applying the proposed approach to IEEE 118-bus test system demonstrate its effectiveness for online power system static security assessment. The comparison of the ANN models with the model based on Newton Raphson load flow analysis in terms of accuracy and computational speed indicate that the proposed model is effective and reliable in the fast evaluation of the security level of power systems. The proposed online static security assessment (OSSA) module realized using the ANN models are found to be suited for online application.

Online Static Security Assessment Module Using Artificial Neural Networks

Thermal behavior analysis of lithium-ion batteries for electric and hybrid vehicles

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.

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

On-line critical contingency selection and ranking based on a composite security index is presented in this article. A new method for obtaining a composite scalar index, in terms of both line flow and bus voltage limit violations, has been developed based on the concept of hyper-ellipse inscribed within a hyper-box. The proposed index provides an efficient method for filtering and ranking the credible contingencies and is defined in such a way that a proper differentiation between the secure and insecure states are possible. The composite security index can completely eliminate the masking problem in contingency selection and ranking. In addition, it is seen that if the system is insecure, the exact location of limit violations in buses and branches can be determined by monitoring the violation vector so that proper preventive and corrective control actions can be taken. Extensive simulations have been done using IEEE 30-bus and IEEE 118-bus systems, and the results show that the proposed approac...

A Composite Security Index for On-line Steady-state Security Evaluation

Design and analysis of battery powered systems depend on effective battery model. Hardware-in-the-loop testing of Battery Management System (BMS) extensively uses battery models. Simulation of Hybrid Electric Vehicles highly rely on the battery models which provides actual characteristics. This paper proposes a performance enhanced battery model which can be included in electric circuit design and analysis. Combined electrical and thermal model replicates the real world performance of battery. Self-discharge is the characteristic of a battery which reduces the stored energy without external connection. Effect of self-discharge on State of Charge (SOC) of battery is considered in the paper. Internal resistance variation with SOC of a battery included in the model further enhances its exactness. Proposed model is validated for Sony US 18650 Li-ion battery in MATLAB Simulink.

Generic Battery model covering self-discharge and internal resistance variation

Power system state estimation with exclusive utilization of synchronous phasor measurements demands that the system should be completely observable through PMUs only. To have minimum number of PMUs, the PMU placement issue in any network is an optimization problem. In this paper, a model for the optimal placement of phasor measurement units (PMUs) in electric power networks is presented Optimal measurement set is determined to achieve full network observability during normal conditions, i e no PMU failure or transmission line outage is considered Observability analysis is carried out using topological observability rules. A connectivity matrix modification based integer linear programming is used as an optimization tool to obtain the minimal number of PMUs and their corresponding locations while satisfying associated constraint Simulation results for IEEE 30-bus and New England 39-bus test systems are presented and compared with the existing techniques, to justify the effectiveness of proposed method In All investigations, the zero-injected buses are considered to obtain the best answers.

Optimal PMU placement using matrix modification based integer linear programming

A new method is suggested for islanding detection in microgrids with huge penetration of non-dispatchable renewable energy sources. Time synchronised data is collected in the form of variables like voltage phasors, frequency and rate of change of frequency using phasor measurement units from various locations. The method then uses weighted difference principal component analysis for considering nonlinearities among variables and for data condensation. Multi-resolution Teager energy operator (MTEO) is employed to Q statistics obtained to detect islanding events. Further, to eliminate false alarms a difference of the obtained signal with its mean (MTEOD) is computed. When it is compared with a threshold which is derived based on upper control limit of Q, can successfully identify islanding events with no spurious detections. The method implemented in DIgSILENT/Power Factory and MATLAB achieved 100% accuracy, precision and reliability with zero non detection zone. Moreover, islanding location has been precisely identified to attain situational awareness of the microgrid. Immunity to noisy conditions in the microgrid has been demonstrated using a four-variable mathematical model. For a faster online prediction decision tree classifier is utilized. Real time performance and prediction capability was evaluated with decision tree technique which shows promising results.

R. Sunitha

Papers

Online Static Security Assessment Module Using Artificial Neural Networks

A Composite Security Index for On-line Steady-state Security Evaluation

Generic Battery model covering self-discharge and internal resistance variation

Optimal PMU placement using matrix modification based integer linear programming

Synchrophasor Data Driven Islanding Detection, Localization and Prediction for Microgrid Using Energy Operator