Wide Area Power Systems Stability, Protection, and Security

In recent days electrical power system experiencing a rapid change thereby inclusion of advanced equipment and expansion of transmission/distribution network. Besides the modernisation of existing power system network, a number of renewable energy sources such as wind parks and solar parks etc., have been integrated to balance growing power demand due to the industrialization and digitalization. However, a secure and dependable operation of power system network is not so easy because of its complex nature in terms of control, operation and maintenance of various components in wide-area network. Inspite of gigantic developments in power system operations, components and protection technologies, today’s power systems are more susceptible to blackouts than ever before. In this context some of the recorded major blackout incidents have been classified according to the time and location as of research reference purpose. As per the survey the frequency of occurrence of blackouts is increased over time. Practically, it is not possible to avoid blackouts completely; though, various research studies and number of research articles were acknowledged that by taking some rationally gainful measures, incidence of the blackouts could be abated and/or their effects could be mitigated. The forthright approach is to minimize the peril of unplanned disturbances thereby extenuating opportune paradigms to the extent that possible, the root causes of the disturbances through analyses and audits followed by initiating various preventive and corrective actions. In view of the preventive and corrective actions to avoid wide-area disturbances, a new paradigm has been embraced with Wide-Area Protection, Control and Monitoring (WAPCAM) system. With the rapidly growing capabilities in advanced computer and communication technologies such as Intelligent Electronic Devices (IEDs) enabled Remote Terminal Units (RTUs), Global Positioning System (GPS) enabled Phasor Measurement Units (PMUs); opportunities are now being available to adopt the Wide-Area Protection, Control and Monitoring (WAPCAM) system. Such systems receive wide-range of data or information e.g. system-wide bus voltages, angles, active and reactive power flows, etc., and by analysing them, can estimate whether the system is at stressed condition or not. By taking coordinated actions, the power system network can be saved from proceeding to total collapse, or even, mitigate the wide-area disturbance effects upon the system. WAPCAM system has different level of hierarchies in realization of preventive and corrective actions such as local feeder level, sub-station level and central/regional level. One of the recommended preventive plans against the wide-area disturbances and the blackouts is Wide-Area Protection and Control (WAPC) system that includes Special Protection Schemes (SPS) (or) System Integrated Protection Schemes (SIPS) (or) Remedial Action Schemes (RAS) based on an advanced communication infrastructure etc. To mitigate the impact of wide-area disturbances, the remedial/corrective actions have been initiated by implementing Wide-Area Stability and Control (WASC) system that embraces power system stabilizers (PSS) and ON-Load Tap Changers (OLTC) and Wide-Area Monitoring and Control (WAMC) system. It also includes out-of-step (OSS) bus splitting and optimal islanding schemes etc. Although; the power system exhibits unstable dynamic phenomena at stressed conditions such as Transient Angle Instability, Voltage Instability and Frequency Instability, the WAPCAM has to bring back the power system to normal restorative condition as soon as possible. This chapter enlightens a comprehensive research review and explicates different type of WAPCAM systems that can address the major blackouts to improve stability, reliability and security of power system networks. A comparative assessment has been explicated by summarizing various recently reported conventional and intelligent schemes. It also enlightens the research insights to power system researchers and protection engineers while planning and designing of stable, reliable and secured power system networks.

Nowadays, the increase in electrical energy consumption and power system restructuring have posed new challenges to the operation, control, and monitoring of power systems. In this situation, the supervisory control and data acquisition (SCADA) system is not enough to ensure power system security and stability. The SCADA is often unable to measure data of all buses simultaneously. In addition, the sampling rate in this system is not enough for some power system applications. Therefore, the information obtained from SCADA does not show power system dynamics properly. In order to improve the power system monitoring, wide area measurement system (WAMS) has been developed to overcome the problems of SCADA system. Phasor measurements units (PMUs) are the main part of WAMS system and it basically consists of three essential processes including collecting, transmitting, and analyzing data. WAMS receives obtained data via a high speed communication links. After data processing and extracting appropriate information, decisions are made to improve the power system performance. Efficient use of power system data to achieve a secure operation strategy is targeted using the WAMS system. Due to the effective role of WAMS and PMUs in the reliable operation of power system, it is necessary to study their concept and applications in this chapter. The history of PMU and its structure is presented in this chapter. In addition, the necessity of WAMS for future power system and its difference from SCADA system have been investigated in this chapter. Different algorithms and application of the WAMS are also introduced in this chapter which can be implemented to improve the performance of the future power system.

Information and communication infrastructures (ICIs) in modern wide-area systems handle the transmitting, receiving, and storing of high-speed, large-volume synchrophasor data. Such infrastructures are important components in modern wide-area systems. Although power systems are becoming rather complex, with the introduction of synchrophasor technology, the highly accurate, high-speed, widely deployed, and time-synchronized phasor measurement units (PMUs) are providing operators and auditors an unprecedented way to understand the complex power systems. Consequently, these advanced PMUs challenge the current information and communication infrastructures. This chapter reviews the basics, challenges, and visions of the ICIs in wide-area monitoring systems (WAMS). We will first overview some important wide-area ICI topics and share our experience in building an efficient, reliable, and secure distribution-level WAMS, FNET/GridEye. We will introduce some key technologies that ensure the efficiency and reliability of such a WAMS. Finally, some outstanding challenges and future directions of the contemporary ICIs are discussed and envisioned.

Wide Area Measurement Systems (WAMS) is a collective technology to monitor power system dynamics in real time, identify system stability related weakness and helps to design and implement counter measures. It uses a global positioning system(GPS) satellite signal to time synchronize from phasor measurement units (PMUs) at important nodes in the power system, sends real-time phasor (angle and magnitude) data to a Control Centre. The acquired phasor data provide dynamic information on power systems, which help operators to initiate corrective actions to enhance the power system reliability. The goals of WAMS are real time monitoring, post disturbance analysis, adaptive protection and power system restoration. The major components of WAMS are Phasor Measurement Unit (PMU), Phasor Data Concentrator (PDC), Global Positioning System (GPS for Time Synchronization of the phasors), Communication channel (Preferably optical fiber cable), Visualization and analysis tools, Wide area situational awareness system and Wide area protection and control. This chapter is going to discuss about the goals and benefits of using PMUs, comparison between PMUs and SCADA system, Detailed description of WAMS components, synchronized PMUS, different kind of applications of WAMS in power sector, Components and operation of PMUS, real time examples of WAMS in power system operation and control.

Phasor Measurement Unit (PMU) is an important device for the power system operation as it provides the synchronized data required for the monitoring, protection, and control of the power system. So, to deploy the PMUs for the power system, their optimal locations are needed to be identified. This paper presents the optimal selection of PMU set from the available sets of PMUs. Firstly, it obtains all possible sets of PMUs required for the complete observability of the power system. Then, it defines four criteria such as System Observability Index (SOI), Restorable Islands Observability Index (RIOI), Critical Bus Observability Index (CBOI) and Critical Line Observability Index (CLOI) for the selection of best PMU set. Later, the Multi-Attribute Decision Making (MADM) techniques such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Compromise Ranking Method (VIKOR) have been used for the optimal selection of PMU set. This selection has been tested on some of the IEEE test systems. The results are then compared to analyze the performance of these four methods.

The damping enhancement of power system oscillations remains one of the challenging current interests for secure and reliable operation. This paper presents a comprehensive overview of a novel control scheme that considers synchrophasors and Power System Stabilizers in coordination with an optimized Load Frequency Control loop in order to resolve the undamped local and wide-area oscillatory troubles. Accordingly, a Robust Fuzzy PSS using local signals is first examined. Additionally, an Inter-Area PSS based on high-sampling rate phasor measurement unit is investigated. In fact, using time synchronized measurements as control input signals will participate effectively in monitoring the energy management process. Thus, another configuration mixing local and remote control inputs of a Mixed-PSS is proposed. Performances of these PSSs are evaluated in coordination with a tuned PI-based load frequency control design under different operating conditions. Results on a modified 9-Bus IEEE test system including DFIG wind turbines are reported in order to justify the proposal’s applicability.

Accurate identification of electromechanical oscillations on power systems and determination of its stability condition is a fundamental process in order to carry out an appropriate control action to prevent the partial loss or complete blackout of the system. However, the non-linear characteristics of measured variables often lead to incorrect information about the development of the electromechanical oscillations, making wide-area monitoring a challenging task. In addition, significant amount of information in extra large power systems is produced, which has to be stored on local servers requiring large amounts of central processing unit (CPU) storage. For these reasons, algorithms for Big Data problems in power systems are required and the methods presented on this chapter introduce some potential solutions. In this context, different data-driving methods based on spectral analysis of linear operator are presented for the analysis of electromechanical oscillations from a spatio-temporal perspective. These algorithms have the ability to process spatio-temporal data simultaneously, making possible to characterize inter-area and global oscillations (from 0.1 Hz to 1.0 Hz). To validate the effectiveness of the proposed approaches, two test systems with different structural and generation capacities are analysed: the Mexican Interconnected (MI) system and the initial dynamic model of Continental Europe from ENTSO-E. First, data collected from a transient stability study on the MI system are used to illustrate the ability of data-driving methods to characterize modal oscillations on longitudinal systems; where several inter-area modes produce interactions of different electrical areas. Then, simulation results from the initial dynamic model of ENTSO-E are analysed to characterize the propagation of its global electromechanical modes across Europe, which have been denominated as the North-South and East-West modes with frequencies of approximately 0.15 Hz and 0.25 Hz, respectively. The second analysis include the interconnection of Turkey (TR) to Continental Europe in December 2010, which derived on the grow of size and complexity of the original system having as result a decrease in the frequency value for the East-West mode and the introduction of a third inter-area mode on the system. The chapter concludes comparing the results of the proposed approaches against conventional methods available in the literature.

The modern power grid protection system should have considerable operational flexibility and resiliency to hedge the variability and uncertainty of high dimensional dependencies. The use of wide-area monitoring systems (WAMS) in the smart grid enables the real-time supervision of power system oscillations. With the help of advanced signal processing methods and big data analytics, time-synchronized phasor measurements can be used to extract valuable information concerning the electromechanical modal properties of power system oscillations. This chapter introduces a novel method for identifying electromechanical inter-area oscillation modes with the help of wide-area measurement data. Variational mode decomposition (VMD) can be considered as a flexible signal processing technique on the wide-area phasor measurements in power oscillation analysis. For the real-time operation, it is challenging to preset the value of the mode number in the VMD process. This issue has been addressed by improving the strategy for VMD, which is presented in this chapter. The first stage involves the use of Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique to generate intrinsic mode functions and gives indexing based on the correlation factor. Depending on the indexing, the mode number is selected for the second stage VMD process. Techniques such as spectral analysis and Hilbert transform are quite suitable for the estimation of modal parameters. The study is based on significant features of power oscillations, such as determination of damping ratio, amplitude, and frequency. The identification and estimation of low-frequency modes have been performed using this improvised VMD technique, and the results have been compared with those obtained using empirical mode decomposition approaches. The proposed approach is also validated using real-time data obtained from load dispatch centers. The results indicate the effectiveness of non-linear, nonstationary analysis methods for analysing the low-frequency modes and provide reliable validation of these algorithms in analysing real-time data patterns.

The electromechanical oscillations in the power system, known as local and inter-area modes, as well as power system oscillations in presence of wind turbines due to its inherent stochastic are two important cases in small signal stability study. Fixed speed (FS) pumped storage power plants (PSHP) similar to other power plants based on synchronous machine experience low frequency power oscillations. Therefore, a power system stabilizer (PSS) is developed for damping these oscillations. However, insufficient damping of these oscillations limited the capacity of energy transfer. On the other hand, state-of-the-art PSHP based on doubly fed induction machine (DFIM) known as variable speed (VS) have different effect on both small signal and transient stabilities of power system. Moreover, PSSs can be more important in multi-machine power grid to be tuned in a precise method. Nowadays, in smart power grids, PSS with wide area signal (WAS) instead of local signal is attended to decrease low frequency power oscillations, and therefore improve the small signal stability of the power system. This chapter intends to consider effect of DFIM and SM-based PSHP with different PSS tuning methods. Aiming at this purpose, a case of 343 MW hydro pump-turbine (HPT) coupled to DFIM with 381 MVA in comparison to the SM with same capacity, i.e., 381 MVA, as well as an aggregated wind farm are applied as the study case. Calculation and simulations are conducted in Digsilent 15.1 under diverse conditions. Also, modified New England test system, including10-machine and 39-bus system, is adopted as a large power network in presence of a wind farm. The results show using PSS with WAS can be a good option for FS-PSHP to improve damping low frequency oscillations.

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.

This chapter presents a comparison of the performance of integral (I) and proportional-integral-derivative (PID) controllers in frequency stabilization or load frequency control (LFC) of two-area interconnected power system considering generation rate constraints (GRCs) with Doubly fed induction generator (DFIG)-based wind energy. Two mathematically models are identified for investigations. Power system model 1 is two-area interconnected power system which contains two identical non-reheat thermal plants without DFIG participation. Whereas, power system model 2 contains two identical non-reheat thermal plants with dynamic participation of DFIG at both areas. Moreover, Harris Hawks Optimizer (HHO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA) are applied to find the optimal values of the controller settings mentioned above. The effectiveness of the proposed controllers, which are optimally designed by several optimization techniques (i.e., HHO, SSA, and SCA) is tested and verified through an interconnected power system comprises two identical non-reheat thermal power plants with/without DFIG participation. Time-domain simulation results of the studied power system with all mentioned optimization techniques are carried out using Matlab/Simulink® software to validate the robustness of the proposed controllers.

As the power system becomes more stressed and the penetration of intermittent renewable energies increase, voltage stability assessment (VSA) becomes a key concern for maintaining and enhancing the security of bulk power systems. Physically, the phenomenon of voltage instability is indeed caused by an uncontrollable drop in system voltage after being subjected to a disturbance. This deterioration may ultimately result in voltage collapse that has been responsible for several blackout incidents. So far, a vast number of methods ranging from simple static techniques to complex dynamic methods have been proposed for performing VSA. More recently, with wide deployment of synchronized phasor measurement units (PMUs), PMU-based wide area measurement system (WAMS) has attracted lots of interests from both academia and industry. In this chapter, recent developments of measurement-based coupled single-port models will be presented for VSA. Generally speaking, the concept of the coupled single-port model is to decouple a mesh power grid into several single-port local equivalent models with considering extra coupling impedances. By collecting real-time PMU measurements in each individual load bus, the reactive power response derived from the extended Ward-type equivalent model can be applied to eliminate the reactive power mismatch of the existing single-port model. Meanwhile, these parameters of the Thevenin equivalent circuit in the existing single-port model will be modified by a mitigation factor to improve the model accuracy of VSA. Since the proposed method is simple, several voltage stability indicators can be easily extended with slight modifications. Simulations are conducted on two test systems, including IEEE 57-bus and IEEE 118-bus test systems, to validate the accuracy of the proposed method.

Voltage instability is a growing threat to the security and the reliability of power grids, especially as the penetration level of intermittent renewable energies increase significantly in recent years. Voltage instability and even voltage collapse will take place as the loss of control of the voltage profiles in a power system. To achieve more efficient voltage regulation in power systems, the hierarchical three-level coordinated voltage control mechanism has been developed recently to prevent voltage collapse through the appropriate management of reactive power sources. This chapter presents recent developments in adaptive secondary voltage control (SVC) by utilizing real-time measurements of power systems obtained from the wide-area measurement system (WAMS). These methods are adaptive in the sense that load disturbances are estimated from synchronized phasors of WAMS in nearly real-time. Thus, these control inputs of SVC can be synthesized to minimize deviations in load voltage profiles under the worst-case scenario. Uncertainties in measurement are also taken into considerations by exploring the maximum likelihood (ML) method to further improve SVC performance. Comprehensive simulations on a variety of IEEE benchmark systems have been performed to verify the feasibility and the effectiveness of these schemes.

Traditionally, voltage stability assessment (VSA) are widely investigated by model-based approaches. Several achievements have been developed along this direction, including continuation power flow methods (CPFLOW), direct methods, and optimal power flow methods. Since precise model information are required and their computations are very demanding, their applications to real-time VSA are challenging, especially when network operating conditions and/or network topology may be always changed. In recent years, with wide deployment of synchronized phasor measurement unit (PMUs), PMU-based wide area measurement system (WAMS) has already attracted lots of interests in investigating VSA in advanced artificial intelligence approaches. By collecting real-time big data from power grids and studying these historical data through statics analytics, some prediction models can be constructed for VSA of the current operation conditions. This chapter presents some recent advances in data mining framework for power system VSA under real-time environments. The proposed framework adapts a new enhanced online random forest (EORF) algorithm to update decision trees (DTs), such as tree growth and replacement. By means of weighted majority voting, one of the ensemble learning skills, DTs in the random forest are able to reach consesus to deal with power system changes. The proposed EORF framework is first tested on IEEE 57-bus power systems, and then is applied to Taiwan 1821-bus power system. Through comprehensive computer simulations, the robustness, the computation speed, as well as the assessment accuracy, of the proposed EORF framework are justified for assessing the power system voltage stability in real-time.

Mal-operation of distance relay imposes serious threats to system stability and a big reason for large scale blackouts. These relays operate in its third zone due to the inability of detecting fault during stressed system conditions. These stressed conditions are load encroachment, power swing, voltage instability conditions, extreme contingencies, etc. Conventional distance relay operates on the basis of local measurements. It calculates the impedance from the relay to the fault point for its operation. Load encroachment and power swing are very similar to the symmetrical fault condition and it is difficult for these conventional relays to distinguish these stressed conditions from symmetrical faults. It is therefore important to make the distance relay intelligent enough so that it will be able to discriminate between a fault and stressed system condition. With the advancement in synchro-phasor technology, the drawbacks of conventional relays have been overcome. The wide-area monitoring system (WAMS) is capable of development of online intelligent techniques that can segregate the stressed system condition from any fault. With these advanced techniques, the mal-operation of distance relays can be avoided and thus wide-area blackouts can be stopped. In this chapter, a new scheme for detecting the zone-III operation of distance relay is proposed to discriminate the stressed system conditions such as voltage instability, power swing, or load encroachment from fault. The proposed scheme is based on the monitoring of active and reactive power of the load buses using WAMS. Various cases are created on WSCC-9, IEEE-14 and IEEE-30 bus system to test the performance of the proposed algorithm. The simulations have been done on the MATLAB Simulink platform. Results shows that the proposed method is helpful to avoid the unwanted distance relay operation under stressed system conditions.

Recently, due to the increasing demand with scarcity in installed production capacities, power systems are being operated closer to voltage stability limits resulting in a higher eventuality of voltage collapse. Thus, fast and accurate monitoring of voltage stability has become an important factor in the efficient operation of modern power systems. In this chapter, two approaches based on the combination of multi-layer perceptron (MLP) neural network and adaptive neuro-fuzzy inference system (ANFIS) with moth swarm algorithm (MSA) have been proposed to monitor voltage stability of power systems using phasor measurement units (PMUs) data. In the proposed hybrid MLP–MSA and ANFIS–MSA models, the MSA algorithm is adopted to optimize the connection weights and biases of the MLP network and to determine the tuning parameter in ANFIS model. To evaluate the prediction capability and efficiency of the proposed models, several statistical indicators such as root mean square error (RMSE), correlation coefficient (R) and root mean square percentage error (RMSPE) are used. Numerical studies are carried out on two standard power systems. The obtained results indicate that the proposed ANFIS–MSA model has the most reliable and accurate prediction ability and deemed to be the effective method to estimate the voltage stability margin of the power system based on measurements from PMU devices.

In this chapter, a wide-area integrated method including a set of algorithms for transmission lines fault analysis is introduced. The proposed method is based on extension and modification of state estimation formulation. Thus, the method is applicable to both symmetrical and asymmetrical networks as well as all fault types including symmetrical and asymmetrical ones. The method exploits the capacities of state estimation formulation and the solution algorithm of weighted least squares (WLS) to reduce the effect of inherent errors on the fault location accuracy and detection and elimination of bad data in the measurement vectors. For this purpose, an error model of the measurement chain including instrument transformers and PMUs is proposed. This model is used to design measuring errors covariance matrix in the state estimation formulation. The performance of the proposed method has been investigated through numerous fault events simulated on different locations of all transmission lines of the IEEE 118-bus test system.

Real-time power system monitoring and assessment leads to two major concern, prediction and evaluation of security and stability of power system. This assists in determination of in-time probable anomaly of the system. However, at the same time it requires real—time technological applications to measure network data at all strategic geographical locations. Synchrophasor technology based wide-area situational awareness ensures power system real—time monitoring and assessment. The chapter proposes real—time data driven Wide-area Situation Analyzer (WASA). WASA first detects an event in the system using synchrophasor measurements and then assesses its vulnerability posed to power network. The vulnerability is measured as severity in terms of first swing transient instability. Level of severity index is developed in terms of generator going out of step. The bus voltage trajectories going away with rest of the system due to generator(s) transient instability are considered. The proposed new approach is based on Center of Frequency (COF) formulated from limited Phasor Measurement Unit measurements. To check for an event existence in the system, a new decision based COF concept is defined. In order to determine the severity of the identified event, a new Predictor Indices (PI) is proposed using COF and PMU measurements. These predictor indices are used in assessment methodology, based on Adaptive Boosting (AdaBoost) of decision estimators. Furthermore, comparative results of proposed wide-area situational analyzer with other machine learning algorithms are also shown. The proposed WASA is instigated on IEEE New England 39 Bus system, successfully validating analyzer performance. The different type of events considered are generation outage, bus outage, load outage and line events. Additionally, if any bus outage occurs due to line faults then it is considered as single severity. The results reflects the efficacy of the proposed analyzer in event detection and its assessment efficiently and effectively with very less computational burden. The ability of the proposed analyzer to identify events quickly and correctly makes it appropriate for real—time applications.

With large scale deployment of non dispatchable renewable energy sources, distributed generators (DGs) have paved way for multiple microgrids. Existing standards stipulate disconnection of DGs in the event of any fault in either utility or microgrid side. However, to ensure reliable power supply from these microgrids, the proposed scheme operates an islanding detection algorithm (IDA) employed at the microgrid control center (MGCC) that acts on the switch at the utility point of common coupling (PCC). The whole microgrid is switched over to an islanded mode on detection of an islanding event. Phasor measurement unit (PMU) data from the utility PCC enables accurate islanding detection using islanding detection monitoring factor (IDMF) and rate of change of inverse hyperbolic cosecant function of voltage (ROCIHCF) along with voltage at the PCC. Mathematical morphological filters are employed to detect any persistent short circuit fault in the microgrid side which may island the DG. For such faults, decision for disconnecting the DG or islanded operation is based on probability of power balance (PoB) and probability of islanding duration in the sub-microgrids, computed at the MGCC. Further, performance of the IDA in microgrids is assessed using a proposed, microgrid performance index (MGPI) considering the uncertainties in NDRES. Suitability of the proposed indices to predict events leading to islanded operation in real time is also validated using decision tree (DT) method. The discrimination capability between islanding and other transient events of DTs, yielded an accuracy of approximately 99.9% for minimum detection time, which proves the prowess of the method in real time scenario. Compared to existing methods, the proposed method promises reduced islanding detection time. Another distinct feature is that time for islanding detection remains same irrespective of power mismatch ratios. The proposed method prevents false alarms for critical non-islanding events with zero non detection zone. Utilizing the proposed method, any redundant DG outage can be avoided, minimizing their down time. An economic analysis of the proposed islanding detection method using wide area monitoring system (WAMS) with minimal PMU deployment has also been studied to reduce the cost of PMU installation without sacrificing the reliability benefit for the customers.

The use of phasor measurement units (PMU) allows us to obtain synchronized measurements of various points in the network and whit them analyze the stability of power systems. This chapter presents an algorithm based on participation factors to estimate generator clustering and to evaluate its application on controlled islanding on a power system, with distributed generation, using the data from PMUs after a severe disturbance. The proposed islanding detection method uses the data obtained from PMUs to represent the dynamics of the entire power system and form a measurement matrix, updated using a sliding window, containing the angles of the voltage phasors. Then, a covariance matrix is computed, and the eigenvalues and eigenvectors of this matrix are obtained. Subsequently, the most energetic eigenvalue is identified, and its participation factors are calculated. The participation factors are used as a contribution measurement of each generator into the most energetic eigenvalue, i.e., they will show the contribution made by each one after a disturbance. The clusters will be formed by generators sharing the same participation level. Controlled islanding condition of the system will be evaluated by using the clustering schemes proposed in the literature.

WAMS infrastructures consist of various elements such as digital metering devices, communication and processing systems, in order to facilitate the operation, monitoring and control of power grids. For smart grids, resilience is a high-priority design requirement, since they must be able to resist in failures at any layer, caused by intentional attacks or unintentional events. In this chapter we review existing approaches in the literature for WAMS resilience. Based on our recent work on dependency analysis for WAMS resilience [23], we describe methodologies that take into consideration both optimization and resilience metrics during WAMS design. We explain how WAMS resilience can be increased by reducing the dependencies of WAMS components and by selectively adding controlled redundancy of measurement units and communication links. Finally, we describe how this resilience model can be extended to also take into account the dynamic structure of the smart grid caused by the integration of renewable energy sources.

Today’s electric power grid is a complex, automated, and interconnected cyber-physical system (CPS) that relies on supervisory control and data acquisition (SCADA)-based communication infrastructure for operating wide-area monitoring, protection, and control (WAMPAC) applications. With a push towards making the grid smarter, the critical SCADA infrastructure like power system is getting exposed to countless cyberattacks that necessitate the development of state-of-the-art intrusion detection systems (IDS) to provide comprehensive security solutions at different layers in the smart grid network. While considering the continuously evolving attack surfaces at physical, communication, and application layers, existing conventional IDS solutions are insufficient and incapable to resolve multi-dimensional cybersecurity threats because of their specific nature of the operation, either a data-centric or protocol-centric, to detect specific types of attacks. This chapter presents a hybrid intrusion detection system framework by integrating a network-based IDS, model-based IDS, and state-of-the-art machine learning-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. We have applied the cyber-kill model to develop and demonstrate attack vectors and their associated mechanisms. The hybrid IDS utilizes attack signatures in grid measurements and network packets as well as leverages secure phasor measurements to detect different stages of cyber-attacks while following the kill-chain process. As a proof of concept, we present the experimental case study in the context of centralized wide-area protection (CWAP) cybersecurity by utilizing resources of the PowerCyber testbed at Iowa State University (ISU). We also describe different classes of implemented cyber-attacks and generated heterogeneous datasets using the IEEE 39 bus system. Finally, the performance of the hybrid IDS is evaluated based in terms of detection rate in real-time cyber-physical environment.