Power System Contingency Research Articles

A literature survey on LFC in a deregulated electricity environment

This paper deals with an exhaustive literature survey on load frequency control (LFC) of a deregulated electricity environment for different market scenarios for various power generating units such as thermal, hydro, wind, diesel, and hybrid power systems. Load frequency controls employ conventional controllers such as I, PI, PID controllers as well as robust control methods such as intelligent controllers for single area as well as interconnected systems. The FACTS controllers are used for enhancing power quality and reliability in interconnected systems. The benefits of various controllers and their limitations in a deregulated electricity environment are discussed in details based on the progressive work in modern power systems. The control strategies are based on NERC standards to reduce wear and tear and overcome the dynamic and transient disturbances during sudden load change. The controllers should also take care of contingencies in the modern power system and restore the system to normal condition and ensure steady state stability.

A Spectral Approach for the Efficient Identification of Power Transmission Network Uncontrolled Separation

Uncontrolled network separation (system island formation) is one of the most critical contingencies in power systems. The integrity of the whole power transmission network is a prerequisite for reliable system operation. Therefore, in order to safeguard the system operation and control, it is imperative to quickly identify the topological changes, especially the formation of system islands. In this paper, we developed a spectral clustering-based approach to efficiently detect the existing or the potential network islands. The core of this approach is a graph-algebraic model, which combines the real-power deliverability and topological information of a power transmission network. Based on an improved spectral clustering algorithm, the proposed approach can efficiently identify the critical situations of the network splitting under multiple line outages. This approach has been successfully tested by using the New England 39-bus and 118-bus systems under different scenarios.

WAMS Cyber-Physical Test Bed for Power System, Cybersecurity Study, and Data Mining

Researchers from various cross disciplinary fields such as power systems, data science, and cybersecurity face two distinct challenges. First, the lack of a comprehensive test bed that integrates industry standard hardware, software, and wide area measurement system (WAMS) components and protocols impedes the study of cybersecurity issues including vulnerabilities associated with WAMS components and the consequences of exploitation of vulnerabilities. Second, a lack of comprehensive labeled Synchrophasor data along with other system related information imposes challenges to the development and evaluation of data mining algorithms that can classify power system cyber-power events. In this paper, a WAMS cyber-physical test bed was developed using a real time digital simulator with hardware-in-the-loop simulation. Commercial control and monitoring devices, hardware, software, and industry standard communication networks and protocols were combined with custom MATLAB, Python, and AutoIt scripts to model realistic power system contingencies and cyber-attacks. An automated simulation and control engine was developed to randomize modeled cyber-power events including power system faults, contingencies, control actions, and cyber-attacks. Scripts were added to capture heterogenous sensor data and create ground truth labeled datasets. The WAMS cyber-physical test bed is capable of simulating various sized power systems and creating datasets without altering the hardware configuration. A WAMS architecture is presented to document the integration of various components. Finally, test bed applications, simulated cyber-power scenarios, the dataset development process, and selected results are presented.

Distributed Design of Local Controllers for Transient Stabilization in Power Grids

In this paper, we develop a distributed method for designing local controllers that perform transient stabilization of dynamic response for deflections caused by contingencies in a large power system where multiple independent system operators exist and manage their corresponding generators. The system models associated with individual system operators should be private and be unavailable for deciding control policies of other system operators. We formulate the controller design problem where each controller is designed using its corresponding local system model and they achieve a desired local transient response performance. Our proposed method based on hierarchical state-space expansion enables us to solve the controller design problem and the resulting system satisfies the requirements. Through numerical simulations for the IEEJ EAST 10-machine power system, the effectiveness of our proposed method is verified.

An adaptive supervised wide-area backup protection scheme for transmission lines protection

Maloperation of conventional relays is becoming prevalent due to ever increase in complexity of conventional power grids. They are dominant during power system contingencies like power swing, load encroachment, voltage instability, unbalanced loading, etc. In these situations, adaptive supervised wide-area backup protection (ASWABP) plays a major role in enhancing the power system reliability. A balance between security and dependability of protection is essential to maintain the reliability. This paper proposes multi-phasor measurement units (MPMU) based ASWABP scheme that can function effectively during faults besides power system contingencies. MPMU is an extended version of Phasor Measurement Unit (PMU). It is an intelligent electronic device which estimates the synchronized predominant harmonic phasors (100Hz and 150Hz) along with the fundamental phasors (50Hz) of three phase voltages and currents with high precision. The proposed ASWABP scheme can detect the fault, identify the parent bus, determine the faulty branch and classify the faults using MPMU measurements at System Protection Center (SPC). Based on these MPMU measurements (received at phasor data concentrator (PDC) at SPC) the appropriate relays will be supervised to enhance the overall reliability of the power grid. Numerous case studies are conducted on WSCC-9 bus and IEEE-14 bus systems to illustrate the security and dependability attributes of proposed ASWABP scheme in MATLAB/Simulink environment. Also, comparative studies are performed with the existing conventional distance protection (Mho relays) for corroborating the superiority of the proposed scheme regarding security and dependability. Comparative studies have shown that the proposed scheme can be used as adaptive supervised wide-area backup protection of conventional distance protection.

Using integrated method to rank the power system contingency

Contingency ranking is one of the most important stages in the analysis of power system security. In this paper, an integrated algorithm has been proposed to address this issue. This algorithm employs neural networks method to quickly estimate the power system parameters and Stochastic Frontier Analysis (SFA) in order to calculate the eciency of each contingency. Network security indices (voltage violation and line flow violation) and economic indices (locational marginal price and congestion cost) have been simultaneously considered to rank the contingencies. The eciency of each contingency shows its severity, and indicates that it a ects network security and economic indices concurrently. The proposed algorithm has been tested on IEEE 14-bus and 30-bus test power systems. Simulation results show the high eciency of the algorithm. Test resultsindicate that predicted quantities are estimated accurately and quickly. The proposed method is capable of producing fast and accurate network security and economic indices, so that it can be used for online ranking.

A new method for power system contingency ranking using combination of neural network and data envelopment analysis

A new method for power system contingency ranking using combination of neural network and data envelopment analysis

Dispatch of Vehicle-to-Grid Battery Storage Using an Analytic Hierarchy Process

The number of electric vehicles (EVs) is expected to increase significantly in the future to combat air pollution and reduce reliance on fossil fuels. This will impact the power system. However, appropriate charging and discharging of EVs through vehicle-to-grid (V2G) operations could also provide support for the power system and benefits for EV owners. This raises the questions of when and how EV battery storage should be dispatched, taking into account both vehicle users' and power system's requirements and priorities, as well as the constraints of the battery system. This paper proposes a novel decentralized dispatch strategy based on the analytic hierarchy process (AHP), taking into account the relative importance of the different criteria, including cost, battery state of charge (SOC), power system contingency, and load leveling. The proposed AHP-based dispatch strategy was tested on an IEEE Reliability Test System with different EV numbers and capacities to investigate the efficacy of such an approach. The simulation results demonstrate the feasibility and benefits of this dispatch strategy.

Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks

Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis.

Risk-Based Security and Control Framework for Power System Operation Under Significant Amounts of HVDC-connected Wind Power Generation

In the context of power system operation, probabilistic techniques can provide a deeper insight into security aspects compared to deterministic approaches, by quantitatively considering power system uncertainties and contingency impact. Moreover, they can be helpful to identify and suggest operators the most adequate control actions to reduce the operational risk. This paper proposes an original framework for risk-based assessment and control of operational security in High-Voltage Alternating Current (HVAC) power systems connected to Multi-Terminal High-Voltage Direct Current (MTDC) networks like those envisaged for the integration of future, large off-shore wind farms. After the presentation of a security assessment methodology based on the concept of risk, the paper investigates three possible preventive control strategies to reduce the risk of high current by exploiting control resources available in the system (specifically, generating units and power injections from MTDC grids). Results of the methodology applied to a test system and to a realistic power system (adapted from the Italian transmission network) are presented and discussed.

Flexible Security-Constrained Optimal Power Flow

Power router technology is maturing to the point of becoming a cost-effective enabler of increased transmission asset utilization. Power routers can enable a desirable increase in control of power systems, especially as infrastructure ages and degrades. This paper presents a formal extension to the traditional security-constrained optimal power flow (SCOPF) algorithm, named flexible security-constrained optimal power flow (FSCOPF), which utilizes power router control in the post-contingency timeframe. The operational improvement is enabled by the fast-response capability of FACTS-based power routers to change setpoints in response to power system contingencies. The FSCOPF algorithm, developed in this paper, allows for power routers to be used as a real-time dispatchable resource. We show that further power system savings are achieved when compared to the traditional SCOPF.

A High-Performance Parallelization and Load-Balancing Approach for Modern Power-Systems

Due to the recent trends of chip-miniaturization, the performance of a single-core is plateauing and hence, improving the performance of serial-execution based legacy code has become challenging. Since the expansion in power system operation continues to increase the number of contingencies to be examined, serial-execution platforms present a crucial bottleneck in analyzing sufficiently large number of contingencies within a reasonably small time for performing power-system stability analysis. This paper presents an approach to parallelize power system contingency analysis over multicore processors using Chapel language. To achieve load-balancing for avoiding wastage of computation resources, the authors use efficient work-stealing scheduling. They discuss the important features of Chapel and design choices which enable us to achieve high performance gains. The approach is evaluated using hundreds of contingencies of a large 13029-bus power system. The authors compare the performance of serial-execution with that obtained using 2, 4, 8 and 16 threads. The simulation results have shown that the approach outperforms a conventional scheduling technique, namely master-slave scheduling and also scales effectively with increasing number of threads. It is believed that the performance gains obtained from the approach would be highly useful for control center operators in analyzing a large number of contingencies and thus taking suitable corrective and preventive action against catastrophic events. Also, the insights gained from these experiments will be useful in several business enterprises.

A flexible framework of line power flow estimation for high-order contingency analysis

Abstract Traditionally, power system contingency analysis involves massive power flow calculations, which are usually based on linear approximation methods, such as dc load flow model or distribution factors based method, for fast speed but with compromised accuracy. Particularly, the accuracy of power flow results can deteriorate with the increase of k given N – k contingency analysis. Consequently, the obtained results may provide misleading information by under estimating the impact of some severe contingencies. In order to effectively implement online N – k contingency analysis, we propose a flexible framework of power flow estimation, where generalized line outage distribution factors ( GLODF s) and ac power flow model are integrated together to formulate a two-stage scheme. At first stage, the Monte Carlo sampling technique is used to generate tables of computing errors for the hybrid ac -( GLODF s/ ac ) method. The achieved error information can then provide useful references in the second stage to select either ac - GLODF s based method or ac power flow model for N – k contingency analysis. Theoretically, the framework can provide significantly enhanced accuracy as well as satisfied efficiency. Finally, comprehensive case studies with the IEEE -118 bus system are given to validate the proposed framework.

Decentralized control and fair load-shedding compensations to prevent cascading failures in a smart grid

Abstract Evidence shows that a small number of line contingencies in power systems may cause a large-scale blackout due to the effects of cascading failures. With the development of new technologies and the growing number of heterogeneous participants, a modern/smart grid should be able to self-heal its internal disturbances by continually performing self-assessment to deter, detect, respond to and restore from unpredictable contingencies. Along this line, this research focuses on the problem of how to prevent the occurrence of cascading failures through load shedding by considering heterogeneous shedding costs of grid participants. A fair load-shedding algorithm is proposed to solve the problem in a decentralized manner, where a load-shedding participant need only monitor its own operational status and interact with its neighboring participants. Using an embedded feedback mechanism, the fair load-shedding algorithm can determine a marginal compensation price for each load-shedding participant in real time based on the proportional fairness criterion, without knowing the shedding costs of the participants. Such fairly determined compensations can help motivate loaders/generators to actively participate in the load shedding in the face of internal disturbances. Finally, the properties of the load-shedding algorithm are evaluated by carrying out an experimental study on the standard IEEE 30 bus system. The study will offer new insights into emergency planning and design improvement of self-healing smart grids.

Dynamic state estimation in power systems: Modeling, and challenges

This paper proposes Extended Kalman Filter (EKF) based dynamic state estimator for power systems using phasor measurement unit (PMU) data. Dynamic state estimation in power systems provides synchronized wide area system history of the dynamic events which is key in the analysis and understanding of the system performance, behavior, and the types of control decisions to be made for large scale power system contingencies. In this paper, 2-axis-fourth-order state space modeling and validation of the synchronous machine is explained in detail. The model is then used for dynamic state estimation using EKF in IEEE 3-Generator-9-Bus Test System. The simulation results show that the model and estimation approach are capable to provide accurate information about the states of the machine and eliminate the noise effects on the measurement signal. The main challenges of dynamic estimation in large power systems are also addressed in this paper.

Under Voltage Load Shedding for Contingency Analysis to Optimize Power Loss and Voltage Stability Margin

Power system contingency is a condition of operation which may be caused due to line outage in a system and could lead to entire system voltage instability. This may further result in voltage collapse leading to total blackout of the system. Therefore, voltage collapse prediction and estimating voltage stability margin is an important task in power system operation and planning. In this paper Line Stability Index Lij utilizing the concept of power flow in a single line is adopted to determine the condition of voltage instability. The purpose of Lij is to determine the point of voltage instability, the weakest bus in the system and the critical line referred to a bus. Analytical approach based technique for load shedding has been developed as a solution for secured operation of power system under various contingency conditions to optimize the power flow in order to minimize the system losses within acceptable limit. To validate the effectiveness of the proposed method simulation has been carried out on IEEE 14 bus system.

PARAGON: an approach for parallelization of power system contingency analysis using Go programming language

SUMMARY With the recent emphasis on N-k contingency analysis (CA) and expansion of power systems, the computational demand of CA has greatly increased to ensure power system security. The serial execution platforms are proving to be insufficient to cater to this demand. This paper presents PARAGON, an efficient approach for parallelization of power system contingency analysis using Go programming language. PARAGON uses Google's Go language to parallelize CA over multiple cores using goroutines. The main contribution of this paper is implementation of work-stealing scheduler on widely used multicore processors using concurrent programming language, which can be easily adopted by power system control centers to accelerate legacy codes. Simulations have been performed using a large power system with hundreds of contingencies, and contingency analysis has been parallelized using 2, 4, 8, 12, and 16 cores. The results show that PARAGON provides large computational advantage over serial execution and also outperforms a conventional scheduling technique, namely master-slave scheduling. Copyright © 2014 John Wiley & Sons, Ltd.

SOCCA: A Security-Oriented Cyber-Physical Contingency Analysis in Power Infrastructures

Contingency analysis is a critical activity in the context of the power infrastructure because it provides a guide for resiliency and enables the grid to continue operating even in the case of failure. In this paper, we augment this concept by introducing SOCCA, a cyber-physical security evaluation technique to plan not only for accidental contingencies but also for malicious compromises. SOCCA presents a new unified formalism to model the cyber-physical system including interconnections among cyber and physical components. The cyber-physical contingency ranking technique employed by SOCCA assesses the potential impacts of events. Contingencies are ranked according to their impact as well as attack complexity. The results are valuable in both cyber and physical domains. From a physical perspective, SOCCA scores power system contingencies based on cyber network configuration, whereas from a cyber perspective, control network vulnerabilities are ranked according to the underlying power system topology.

Modeling and Simulation of Compressed Air Energy Storage (CAES) System for Electromechanical Transient Analysis of Power System

CAES system electromotor transient model was established from the view of equipment and the grid interface, based on Power System Analysis Software Package (PSASP). The model was divided into energy storing sub-system model part and generating sub-system model part, and the normal and abnormal dynamic characteristics after CAES connected to the grid were simulated bythis model. The validity of this model was theoretically verified by applying it into example systems and testing.,according to detecting the system responses caused by power system contingency and the sudden change of CAES characteristics. This model provides a basic platform for studying CAES system characteristics, and also provides necessary technical support to the security and stability of power system operation when integrating renewable energy into the grid.

Power System Contingency Ranking using Fuzzy Logic Based Approach

Voltage stability is a major concern in planning and operations of power systems. It is well known that voltage instability and collapse have led to major system failures. Modern transmission networks are more heavily loaded than ever before to meet the growing demand. One of the major consequences resulted from such a stressed system is voltage collapse or instability. This paper presents fuzzy approach for ranking the contingencies using composite-index based on parallel operated fuzzy inference engine. The Line Flow index (L.F) and bus Voltage Magnitude (VM) of the load buses are expressed in fuzzy set notation. Further, they are evaluated using Fuzzy rules to obtain overall Criticality Index. Contingencies are ranked based on decreasing order of Criticality Index and then provide the comparison of ranking obtained with Fast Voltage Stability Index (FVSI) method.