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The design of future distribution systems involves the application of flexible technologies such as renewable-based distributed generations (DGs), battery energy storage systems (BESSs), demand response for controllable load management and distribution network reconfiguration for achieving assets optimisation and for improving the efficiency of the distribution systems. The renewable-based DGs are the source of uncertainty that can be overcome by the proper modelling of renewable resources and application of energy storage devices. However, the coordination of these flexible technologies is essential to avoid counterproductive results and to extract maximum possible benefits from these technologies. This work, therefore, aims to study the coordinated impact of controllable load with renewable-based DGs, BESSs and network reconfiguration for improving the performance of distribution systems. The coordination of these technologies is a very complex optimisation problem due to various constraints associated with charging and discharging of BESSs, complex nature of controllable load management and feeder current limits of distribution network. In this study, an improved water evaporation optimisation algorithm is developed to solve this multi-constraint complex optimisation problem to minimise the network loss and voltage profile improvement for distribution system. The results show that this coordinated operational problem significantly improves the performance of distribution systems.

In this study, a multiyear distributed generation (DG) and battery energy storage system (BESS) investment planning with the coordination of demand response (DR) is presented for the distribution s...

Reliability assessment of modern power systems is crucial for planning and operation. The reliability of power system can be analyzed by determining the system state using reliability indices and considering component data and configuration. These indices are probabilistic measure of the ability of the generating unit(s) to supply the load demand. In this paper, reliability assessment is proposed for a composite power system using Monte Carlo Simulation (MCS) and Artificial Neural networks (ANN) based hybrid approach. ANN improves the computational efficiency and reduces the residue error of sampled load points obtained from MCS. A comparative analysis has been carried out by applying different ANN based algorithms. The different reliability indices determined are LOLE, LOLP, EENS, PLC and EIR. The efficiency of the proposed methodology is investigated on IEEE 24 bus Reliability Test System (RTS).

Introduction: Majority of lung cancer presents at an advanced unresectable stage, emphasizing the diagnosis on cytological specimens like EBUS-TBNA. This study was aimed to subtype lung carcinomas on EBUS-TBNA specimens on smears and cell blocks (CB) and to assess the utility of limited panel ICC towards this subtyping. Materials and Methods: This is both retrospective and prospective study done on total of 142 cases for two and half years. The samples from either lung or mediastinal lymph nodes diagnosed as lung carcinoma on cytomorphology in correlation with clinico – radiological features were included. Extrapulmonary metastatic tumors, lymphomas and malignancies other than carcinomas were excluded. The cytologic material (EBUS-TBNA smears and CB) was obtained by EBUS TBNA procedure done on lung lesions and/or mediastinal lymph nodes and stained with required stains. All cases were first classified as small cell carcinoma (SCC) and non small cell carcinoma (NSCC) on cytomorphology as per 2004 WHO classification. NSCC was then further subtyped. ICC with TTF1/p63 antibodies was applied on NSCC and synaptophysin on SCC/neuroendocrine tumors. Napsin A was applied on p63/TTF1 negative cases. The results were expressed in frequencies and percentages. Result: On cytomorphology alone, 25 of 142 cases (17.6%) were classified as SCC and 117 cases (82.4%) as NSCC. NSCC was further subtyped on cytomorphology (smears+CB) into definitive (squamous cell carcinoma [SqCC]/adenocarcinoma [ADC]) v/s favouring (favouring SqCC/ ADC/ adenosquamous carcinoma) v/s not otherwise specified (NOS) in 41 (35.1%) v/s 33(28.2%) v/s 43(36.7%) cases respectively. Immunostains TTF1/ p63 and napsin A applied on possible 86 NSCC cases reduced NOS to 9.3 %. Synaptophysin confirmed all 20 cases of SCC and 1 large cell neuroendocrine carcinoma (LCNC). Overall in 142 cases, limited panel ICC reduced NSCC-NOS to 11.9%. Conclusion: Subtyping of lung carcinoma on EBUS- TBNA samples is feasible. Preparat

Background and study aims Although endoscopic ultrasound (EUS)-guided fine-needle aspiration (EUS-FNA) is an established modality for pathological sampling of pancreatic and biliary lesions, limited data are available on the diagnostic value of EUS-FNA for evaluation of gallbladder mass lesions, a common cause of obstructive jaundice. We aimed to evaluate the usefulness of EUS-FNA for diagnosis of gallbladder mass lesions presenting with biliary obstruction. Patients and methods This study was a retrospective analysis of data from patients who had undergone EUS-FNA for gallbladder mass lesions. FNA was performed on either a gallbladder mass, metastatic node or liver lesions. Outcome measures were diagnostic yield of EUS FNA and adverse events. Results From April 2011 to August 2018, 101 patients with gallbladder mass lesions with biliary obstruction underwent EUS-FNA. The final diagnosis was malignancy in 98, benign disease in one, and two patients were lost to follow-up. EUS-FNA confirmed the diagnosis in 89 of 98 patients with malignancy (sensitivity 90.81 %); was false negative in nine of 98 cases with malignancy; and was truly negative in the solitary patient with benign disease (specificity 100 %). Positive predictive value, negative predictive value (NPV), and accuracy were 100 %, 10 %, and 90.90 %, respectively. Two patients had self-limiting pain. Conclusion EUS-FNA is a sensitive tool for evaluation of gallbladder mass lesions presenting with obstructive jaundice. However, because of low NPV, lesions in which FNA is negative should be further evaluated.

The adoption of environmentally friendly electric vehicles (EVs) is increasing over the years due to the increased awareness of energy and environmental challenges. However, the current growth is slow because of lack of proper charging infrastructures. This study proposes a multi-objective synergistic planning model of an EV charging station considering the interaction between the distribution system and transportation networks as the fast charging of EVs affects the operation of both networks. The developed model minimises the power losses and voltage deviation of the distribution system and maximises the EV flow served by the fast charging station (FCS) simultaneously taking into account permissible waiting time and service radius of FCS. Multi-objective grey wolf optimiser (MOGWO) algorithm is used to obtain the non-dominated solutions and fuzzy satisfaction-based decision-making method is employed to reach final planning scheme. The effectiveness of the proposed model is investigated on the IEEE 123-bus distribution system coupled with a 25-node transportation network. The influence of different objectives, service radius and waiting time on the planning of FCS is also explored. Results reveal that the developed method can provide rational siting and sizing of FCS and it is also found that proper service radius and waiting time provide more convenience to the customer.

This study addresses a demand response programme (DRP) model considering the price elasticity of demand to determine the peak scheduling for different categories of consumers with the possibility of load shifting. The main objective is to minimise daily energy loss and improvement in the node voltage profile of distribution system along with the economic benefits of different stakeholders. The proposed work helps in appropriate selection of DRP for different feeders/consumers. The investigations are performed on a benchmark 33-bus test distribution system and comprehensive analysis is illustrated through simulation results.

The increasing wind integration in present grids to generate clean energy escalates the problem of low-frequency oscillations (LFOs) in the system. Here, a wide area-based coordinated control strategy of power system stabilisers (PSSs) and doubly fed induction generator's (DFIGs) power oscillation dampers (PODs) is presented to improve the system damping. An improved eigenvalue-based objective function is employed to optimally tune the variables of the controller using whale optimisation algorithm (WOA). The impact of DFIG location and thus the suitable wind farm locations are also found out using eigenvalue analysis and dynamic sensitivity analysis. The results are investigated on IEEE 68 bus test system highlighting the effectiveness of proposed strategy for a wide range of operating scenarios.

With the increase in local grid penetration, solar power technologies have experienced strong energy market growth in the last few years. This has put emphasis on the need for accurate forecasting. In this paper, various models are proposed for predicting solar irradiance for the location Bhadla, Jodhpur, India and a comparative analysis is done. The forecasting is proposed using different statistical models such as stochastic MARKOV model, Auto-regression Integrated Moving Average (ARIMA) model and Artificial Neural Network (ANN) model.

Solar power technologies have emerged as a strong participant in energy market growth. In the past few years, there has been a corresponding increase in its grid penetration. Accurate short-term solar forecasting is very important for scheduling of the solar parks. The paper investigates the use of the Markov Chain Model for solar irradiance forecasting for a short-term period (hourly day-ahead solar irradiance forecast for an individual solar park). The proposed model depends on only one variable, i.e., Solar Irradiance. The geographical station considered for this study is taken at Bhadla, Jodhpur, Rajasthan, India. The performance of the proposed method is evaluated by calculating different statistical error measures like Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

This paper investigated the impact of different battery energy storage (BESS) operational strategies with demand response (DR) and wind-based renewable generation to enhance the performance of the distribution network. The optimal coordination of multiple BESSs, DR, and wind generation is used to minimize network energy loss. This coordinated operational problem is subjected to the distribution system constraints such as feeder current limit, voltage range, and power balance. The suggested methodology is tested on the IEEE 33-bus radial distribution network. A genetic algorithm (GA) is used to investigate the different operational strategies. The results show that the optimal coordination problem can reduce the system losses and also maintain the voltage within the specified limits. The implementation of BESS with DR and wind reduces 62.42% of network losses and it helps the distribution system operator for the selection of BESS operation strategy when the demand of the consumers is controllable.

This paper investigated an effective water evaporation optimization algorithm based approach to improve the effectiveness of the distribution network. The optimal accommodation of multiple photovoltaic (PVs) generation source and its coordination with network reconfiguration is used to minimize the network energy loss. The coordination of PVs and dynamic network reconfiguration of the distribution network is a complicated optimization problem. The problem of dynamic reconfiguration of the distribution network is mixed-integer, non-differentiable, and of an extremely complex combinatorial nature. Besides, the constraint of radiality usually rises the complexity of the evolutionary meta-heuristic optimization algorithms. The developed methodology is based on the foundation of graph theory, which limits the search space and also prevents repetitive mesh checks. The developed methodology is proficient and ensures that only possible radial topologies are generated. This coordinated operational planning is subjected to the distribution system constraints such as limits of feeder current, limits of node voltage, and power balance, etc. The suggested methodology is tested on the IEEE radial 33-bus distribution system. The results show that optimal accommodation of multiple PVs and its coordination with dynamic network reconfiguration reduces the network loss to 20.24% and also maintains node voltage within the specified limits.

With rapid proliferation of wind generation in current generation mix, the issue of low frequency oscillations (LFOs) may get escalated in the modern power grids. The eigenvalue and dynamic sensitivity analysis have been employed to examine the effect of wind integration on system damping. Further, a wide area based robust damping improvement control is suggested. It involves the coordinated control of power system stabilizers (PSSs) of synchronous generators (SGs) and power oscillation dampers (PODs) of doubly fed induction generators (DFIGs). The robust control is attained by employing a new fitness function based on eigenvalue and damping ratio and optimized by Whale Optimization Algorithm (WOA). The wide area POD inputs are selected using modal observability criterion, obtained using phasor measurement units (PMUs) located optimally in the system. The results are verified on IEEE benchmark 68 bus NY-NE (New York- New England) test system. The simulation results highlights the robustness of proposed control to changing system conditions and shows its effectiveness in augmenting system damping and thus small signal stability with high level of wind penetration.

Objectives: To report the diagnostic yield and safety of endoscopic ultrasound-guided fine-needle aspiration (EUS FNA) for the evaluation of proximal bile duct lesions. Materials and methods: A retrospective analysis of data of the patients, who had undergone EUS for proximal bile duct lesions was done. FNA was performed from either the bile duct mass, enlarged node, or liver lesions. Outcome measures were the diagnostic yield of EUS FNA and adverse events. Results: From April 2011 to August 2018, 147 patients with suspected proximal bile duct malignancy underwent EUS. Mass lesion was seen in 133 (90.47%) patients. FNA was performed in 125 (85.03%) patients. The final diagnosis in patients undergoing EUS FNA was malignancy in 118, benign disease in six, and one patient was lost to follow up. EUS FNA confirmed the diagnosis in 103/118 patients with malignancy (sensitivity 87.28%); was false negative in 15/118 cases with malignancy, and was truly negative in all the six patients with benign disease (specificity 100%). Positive predictive value, negative predictive value, and the accuracy of EUS FNA were 100%, 28.57%, 87.90% respectively. No serious adverse event was reported, five patients had self-limiting pain. Conclusion: EUS FNA is a sensitive tool for the evaluation of proximal bile duct lesions. Low negative predictive value warrants further evaluation in patients with non-malignant findings on cytology.

Due to environmental concerns and to meet the increasing load growth, the renewables penetration, mainly wind, is increasing rapidly. This leads to changes in system configuration and load flow. Due to this the dynamic interaction due to converters increases, and this amplifies the low frequency oscillations (LFOs) issues in the grid. The study presented analyzed the impact of doubly fed induction generator (DFIG) wind farms location on the small signal stability of the system. The analysis is performed using well established modal analysis technique. The study is carried out on the IEEE benchmark 39-bus test system. The investigations performed can be utilised for the initial phases of grid planning to be integrated with DFIG wind farms.

Electric vehicle (EV) integration into the power grids is increasing rapidly. To analyse the effect of charging of EVs on the distribution system, most of the literature considered EV load as constant power load (CPL) which do not represent the exact behaviour of these uncertain loads. An accurate EV load modelling is developed by determining the relationship between power consumption by EV, grid voltage and state of charges of fast charging EV load. The derived relationship is validated by simulating a realistic fast charging system to obtain a battery charging behaviour characteristics and is curve fitted on standard exponential load model. Further the impact of stochastic 24-h load profile of fast charging EVs considering the exponential load model is investigated on IEEE 123 bus distribution system and is compared with the constant impedance-constant current-constant power (ZIP) load model and CPL model. The stochastic 24-h load is developed using queuing analysis-based method. The results show that the exponential load model is the better representation of fast charging EV load and 10.19% of the reduction in annual energy demand and 11.19% of the reduction in annual energy loss is observed for exponential load model compared to the existing CPL model.

Inception of any disturbance may edge the power system towards transient unstable settings. To safeguard the system from transient instability, accurate and quick assessment of impact of disturbance on the system is essential. The majority of available state-of-art presents phasor measurement unit (PMU) assisted Transient Stability Assessment (TSA), which does not infer any information regarding the severity of disturbance on the system. This paper proposes model free Wide Area Transient Instability Severity Analyser (WATISA) for real-time severity assessment effectively. The WATISA presents first swing TSA in terms of severity assessments. Following an event, severity assessment is quantified by assessing the number of severely affected buses connected with generators and loads. The presented method first, utilizes synchronized bus frequency and voltage measurements to construct a novel Robust Severity Assessment Index (RSAI) to capture the real-time status of a power network. Then, to determine the severity of an event, Severity Indices (SI) based on RSAI and bus voltage measurements are calculated. Finally, Adaptive Boosting (AdaBoost) of decision estimators is developed to evaluate associated Severity Level (SL) for incurred disturbance. To validate robustness of the proposed WATISA, its performance is tested with system topological changes and wide-area measurement noise. The simulation results on IEEE 39 bus power system highlights method effectiveness in estimating severity level with topological changes and measurement noise.

The increasing integration of wind turbines mainly doubly fed induction generators (DFIGs) introduces dynamic interaction with the conventional synchronous generators (SGs) affecting the damping of low-frequency electromechanical oscillations (LFEOs) in the system. This study presents a complete impact analysis of DFIG integration on system damping. The damping ratio sensitivity to inertia is used to evaluate the impact of DFIG integration on the critical modes. This study identifies that the impact could be positive or negative based on the location of DFIG. The results obtained are verified by detailed eigenvalue analysis and time-domain simulations for different operating conditions with three-phase fault. The negative impact on damping is improved by optimally tuned power oscillation dampers (PODs) with input signals obtained from the phasor measurement units. The impact on dynamic performance of the system due to removal of SGs and addition of DFIGs is investigated. The DFIG penetration is increased on the basis of participation of SGs in critical modes and the results of sensitivity analysis. The proposed approach is tested on standard IEEE 39-bus test system. The investigations carried out in this work can be employed for planning studies of power system integrated with DFIG for identifying their suitable locations and improving LFEO damping with their high penetration.

The DIgSILENT Power Factory is an engineering tool for the analysis of distribution, transmission, and industrial electrical power systems. “DIgSILENT” is an acronym for “Digital SImuLation of Electrical NeTworks”. This software is an advanced integrated and interactive package dedicated to electrical power system and control analysis so as to achieve planning and operational optimization objectives. The package can be interfaced with programming languages and software for performing tasks for better utility. Python is a high-level programming language with legible syntax and comes with open-source license. This paper brings the methodology for interfacing of Python with DIgSILENT Power Factory for automation of tasks. The paper provides various code snippets for interfacing Python with software package and task automation.

Synchrophasor measurements assisted real-time wide area monitoring and control of power system is a challenging task. Transient stability status of a power system following a disturbance is an important aspect of wide-area monitoring. This paper presents methodology of PMU-assisted Naive Bayes classification to predict transient stability state of power system following a disturbance. The optimum PMU placement is obtained using Integer Linear Programming (ILP). The input given to the classifier is Frequency records prior to fault through first six cycle consecutive data obtained from optimally placed PMU following a large disturbance and output is the stability status of the power system. The proposed methodology is tested on New England IEEE 39-bus test system implemented on DIgSILENT Power Factory and Python. The results obtained for unobserved operating conditions shows the proposed methodology predicts the real-time stability status of the system and with less computational effort.

With the increase of renewables in the generation mix, the problem of low-frequency oscillations (LFOs) further escalates in modern power systems. This paper proposes a method for LFO modes monitoring and coherency identification using Phasor Measurement Units (PMUs) and Artificial Neural Network (ANN) in real time for a high wind-penetrated power system. The data for ANN training and testing is obtained from PMUs that are placed optimally in the system for complete system observability. The synchronously sampled PMU data is dimensionally reduced using Principal Component Analysis before using it to train the ANN. The proposed approach is verified on the IEEE New England benchmark system. The results obtained validate the effectiveness of the proposed approach in predicting system damping and coherency status with very less computational burden under varying operating conditions.

Encouragement of Electric Vehicles (EVs) due to environmental concerns calls for proper planning of charging infrastructure to promote its successful deployment. The charging infrastructure will affect both the transportation and distribution network. This paper proposes optimal siting and sizing of EV Fast Charging Station (FCS) with the aim of capturing as many charging demands as possible with consideration of customer convenience and satisfying the transportation and distribution system constraints. Therefore, the spatial-temporal charging demand is first identified and based on the queuing model, the size of the probable FCS is determined to minimize the number of FCS slots within the allowed waiting time. Then the constrained Mixed Integer Nonlinear Programming (MINLP) problem is formulated to capture the maximum EV flow and solved using Binary Grey Wolf Optimizer (BGWO) algorithm. The effectiveness of the proposed planning approach is investigated on IEEE 123 bus distribution system coupled with 25-node transportation network.

In this study, coordination of multiple distributed energy resources (DERs) is investigated to address the techno-economic aspects of distribution network operation. This paper essentially aims to find optimal dispatches of battery energy storage systems (BESSs) in coordination with demand response (DR) in the presence of stochastic wind generation and shunt capacitor to minimize distribution power loss and grid demand cost. A multi-objective problem is formulated to simultaneously minimize power loss and grid demand cost in coordination with a time of use based DR program while maintaining node voltage deviation within limits. Moreover, the effect of uncertainty associated with wind power generation is also modeled to analyze the considered multi-objective problem in a more practical way. To solve this complex optimization problem, the non-sorted genetic algorithm (NSGA-II) with the technique for order of preference by similarity to ideal solution (TOPSIS) is adopted. TOPSIS is used to select the most compromising solution from the Pareto optimal front of NSGA-II. For validation of proposed strategy, the DER coordinated operational problem is tested on benchmark 33-bus and practical 108-bus Indian radial distribution network.

Need of reduction in CO 2 emissions and increased oil demand in automobile sector resulted in development of Plugin Electric Vehicles (PEVs). It has a rechargeable battery and can act as storage device. This mobile battery storage device is utilised to implement Vehicle 2 Grid (V2G) technology. In this paper, the mathematical model has been suggested to serve the grid in term of ancillary service such as frequency regulation. The regulation signal from PJM is used to simulate the proposed scheme. To meet the regulation signal effectively, thousands of PEVs have to participate in this scheme. So, the idea of the aggregator is integrated into the proposed scheme to control the fleets of PEVs. The regulation signal is used to determine the total charging and discharging power of PEVs to regulate the frequency. The results show the proposed scheme is able to meet the high negative regulation signal with G2V action due to availability of PEVs for charging. But it fails to perform properly during high positive regulation signal with V2G action due to unavailability of PEVs for discharging. The arrival of PEVs at the Charging Station (CS) is governed by mobility data of Korean Transportation Safety Authority.

The wind integration is on the rise in modern grids to generate cleaner energy due to increasing environmental concerns. This further escalates the problem of low frequency oscillations (LFOs) in power system. Thus, real-time monitoring of oscillations becomes even more important in present interconnected power systems. The conventional methods are offline and time consuming. In this work, a wide-area based method employing Phasor Measurement Units (PMUs) and Artificial Neural Network (ANN) is proposed to predict the system oscillatory status in real-time. The PMUs are optimally placed using modified Integer Linear Programming. The PMU data is dimensionally reduced using Principal Component Analysis before using it to train the ANN. The ANN predicts the LFO related information like damping ratio and frequency and an index to access the mode’s localness. The proposed methodology is verified on IEEE New England benchmark system. The suggested method is very fast and accurate in predicting the required information in real-time for different operating conditions involving topological variations with very less computational requirement.

Wind is one of the most important parts of renewable energy sources and optimal scheduling of wind power in wind farms is essential. Therefore, accurate prediction is a necessary task to be done in order to have a clear picture of how this wind energy can be utilized to its maximum potential. In this paper, wind speed is forecasted using it as a signal for wavelet transformation and the coefficients are predicted to obtain the forecasted wind speeds. The geographical location under study is taken at Jodhpur in Rajasthan, India. The performance evaluation of the proposed method is calculated using the different statistical error measures like RMSE, MAPE and MAE.

Real time monitoring and control of low frequency oscillations is an important issue to be taken into consideration in modern interconnected power systems operation. A method to find the system small signal stability and coherent generators following a disturbance in real-time is proposed in this work The synchronously sampled data available from Phasor Measurement Units (PMUs) at a high sampling rate provides an opportunity to observe the system operation near to real-time. The first four cycles of post disturbance data comprising of bus voltage magnitudes and angles is measured from optimally placed PMUs. This data is fed to different Artificial Neural Networks to determine the damping index and coherent groups. The dimensionality reduction is performed using Principal Component Analysis. The suggested method is very fast and accurate in predicting the system damping and coherent groups in real-time for different operating conditions including topological variations and 3-phase faults. The efficiency of the proposed methodology is investigated on IEEE 39-bus test system.

Research activities and data collection of metals present in living organisms are called as “metallomics”. In metallomics, biomolecules incorporating metal ions viz. metalloenzymes and metalloproteins, are known as “metallomes”. Metallomics aims to identify metallomes of living organisms and to annotate the physiological significance as well as the biological functions. However, in order to ascertain metallomics to be the part of biometal science, recent analytical technologies like chemical speciation are required to analyze the metallomes. Environmental applications like bioleaching, phytoremediation of soil by using microbes, and to deal with the uptake, transport, storage of trace metals necessary for protein functions and biomarkers identification under ecotoxicological studies really require metallomics involvement. As an interdisciplinary research area, metallomics cover plant and animal physiology, nutrition and become a potential candidate in pharmacology, biogeochemistry and clinical chemistry. Metallomics uses analytical and spectroscopic methods to find the quantitative and qualitative information about metal ions that are present as ligands a well multifaceted biological matrix in trace amounts or occur as non-covalent complexes in order to perform different biological processes. Latest spectroscopic methods along with in-silico approaches including bioinformatics are the important tools needed for research activities in metallomics. The present review highlights the basics of metallomics in biological sciences and its emergence as a novel omics era in relation to other fields. Besides the above aspects, applications and future prospects of metallomics have been highlighted.

In order to meet the growing demand of energy, renewable resource utilization has increased in recent years. Wind is the source to a significant percentage of renewable resources and wind farms harvest this energy into electricity with the help of wind turbines. These turbines are very costly to set up and require high amount of maintenance. Accurate short term (from 30 minutes up to 6 hours ahead) wind energy forecasting is therefore important for optimal scheduling of the wind farms. The paper explores the usage of Markov Chains for forecasting wind speed during a short-term period (day-ahead hourly wind generation forecasts for an individual wind farm). The proposed prediction model depends on one variable factor - wind speed, for a specific wind turbine. The geographical location under study is taken at Jodhpur in Rajasthan, India. The performance evaluation of the proposed method is calculated using the different statistical error measures like RMSE, MAPE and MAE.

Summary In this paper, a unified approach for transient stability enhancement following large disturbance is proposed through preventive or/and emergency control measures. Transient stability-constrained generation rescheduling is proposed as a preventive control strategy with participation of only few generators on the basis of the rotor angle swings. However, if generator rescheduling fails to regain the transient stability of the power system, heuristic real-time transient stability assessment scheme is proposed through synchrophasor measurements. If the system is predicted to be unstable in real-time, emergency control actions like generator tripping and load shedding are proposed with information about their initiation, location, type, and the magnitude. These emergency actions are purely based on the voltage magnitudes and phase angles obtained through phasor measurement units in real-time. The effectiveness of the proposed scheme is demonstrated on IEEE-39 New England test system, and the results obtained are promising. Copyright © 2015 John Wiley & Sons, Ltd.

The increasing need to improve transient security assessment of existing or forecasted operating conditions of networks by power system operators is major concern of the power system security monitoring problem at the Energy Management Systems. This paper proposes a preventive control of transient stability with generation rescheduling based on rotor trajectory index obtained using time domain simulations. This index may help power engineers in making operational decision and to obtain a generation configuration with better transient security dispatch. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus New England system for a three phase fault at different loading conditions with single and multiple line outage cases.

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.

The deployment of Phasor Measurement Units (PMUs) are important for proper monitoring and control of modern power systems. In this paper, Optimal PMU Placement (OPP) problem is solved to obtain the complete observability of power system with minimum number of PMUs under normal and abnormal operating conditions. The proposed OPP problem determines coordinated observability considering multiple contingency cases: single line outage, double line outage resulting in bus isolation, single PMU loss resulting in communication failure. Integer Linear Programming (ILP) is used to determine optimal number and locations for OPP problem. Moreover, Optimal PMU placement problem with conventional measurements is modified for incorporation of bus isolation caused due to contingency. The effectiveness of proposed methodology has been tested on IEEE 14-bus system and New England 39-bus power system. The results highlight the practical suitability of the proposed approach at the control centers.

The complete state of the power system can be estimated by optimal number and proper location of Phasor Measurement Units (PMUs). In this paper, an Optimal PMU Placement (OPP) problem that incorporates intact base case conditions and single PMU loss (P-1) contingency resulting in communication failure is formulated. Integer Linear Programming (ILP) is simulated to get optimal number and locations of PMUs in OPP problem. The proposed method is investigated on four test systems: Brazilian 7-bus equivalent system, IEEE 14-bus system, New England 39-bus power system and 145-bus 50 generator power system.