Wide-area Power System Research Articles

Real-Time Detection and Localization of Line Trip Event via Relative Phase Angles

Line trip events widely exist in power systems. They can result in power outages and a huge economic loss if not promptly detected and localized. To provide a fast and precise solution, this paper presents a Complete Coverage of Voltage Measurement (CCVM)-based line trip event detection algorithm and a Relative Phase Angle (RPA)-based line trip event localization algorithm. First, frequency and relative phase angle features during a line trip event are calculated. Then, the CCVM-based algorithm is proposed from both frequency and rate of change of frequency estimation algorithm aspects. Additionally, the RPA-based algorithm is presented, and two cases are studied to demonstrate the uniqueness of the proposed algorithm. Various experiments are conducted, where the simulation results demonstrate that the proposed CCVM-based algorithm can detect a line trip event as short as 2.07 ms. In addition, the RPA-based algorithm has 1.26 times higher localization accuracy compared with the frequency magnitude-based and phase angle-based algorithms. The experiment results on the examples from two interconnected power systems in the U.S. verified the performance of the proposed algorithms in wide-area power systems.

Design of a Wide-Area Power System Stabilizer resilient to permanent communication failures using bio-inspired algorithms

The operation of power systems requires a set of requirements and studies to ensure the continuous and safe supply of electricity to consumer centers. In small-signal stability studies, low-frequency oscillation modes need to be evaluated as they can compromise the operation of power systems if they are not properly damped. Many structures and control devices have been proposed to mitigate these oscillation modes and the Wide-Area Power System Stabilizers (WAPSSs) have proven to be effective in improving the damping rates of these modes as they use remote system signals from Phasor Measurement Units (PMUs). However, cyber-attacks and communication failures can affect WAPSS communication channels and affect its operation in the power system. This article proposes a method based on a multiobjective optimization model for WAPSS design that is robust to the permanent loss of one WAPSS communication channel. Bio-Inspired Algorithms are applied and compared to solve the optimization problem. A set of case studies were conducted and discussed for the IEEE 68-bus system through modal analysis and time domain simulations. The achieved results showed the need for a fault-robust damping controller and the WAPSS-type controller was able to guarantee good dynamic performance regardless of permanent communication failures. Furthermore, different bio-inspired algorithms provided different results for the closed-loop control system when applied to the proposed optimization model.

Design of a Wide-Area Power System Stabilizer to Tolerate Multiple Permanent Communication Failures

Wide-Area Power System Stabilizers (WAPSSs) are damping controllers used in power systems that employ data from Phasor Measurement Units (PMUs). WAPSSs are capable of providing high damping rates for the low-frequency oscillation modes, especially the inter-area modes. Oscillation modes can destabilize power systems if they are not correctly identified and adequately damped. However, WAPSS communication channels may be subject to failures or cyber-attacks that affect their proper operation and may even cause system instability. This research proposes a method based on an optimization model for the design of a WAPSS robust to multiple permanent communication failures. The results of applications of the proposed method in the IEEE 68-bus system show the ability of the WAPSS design to be robust to a possible number of permanent communication failures. Above this value, the combinations of failures and processing time are high and they make it difficult to obtain high damping rates for the closed-loop control system. The application and comparison of different optimization techniques are valid and showed a superior performance of the Grey Wolf Optimizer in solving the optimization problem.

Inertia estimation of power system with new energy considering with high renewable penetrations

The emerging energy technologies, such as wind energy and photovoltaic (PV), will gradually replace the traditional synchronous generator in wide-area power system. As the wind, PV and energy storage equipment are all controlled by power electronic inverters, which are decoupled from the system and cannot provide effective inertial support to the power system with new energy (PS-NE), resulting in stability problems caused by the low inertia of the PS-NE. In particular, this paper investigates the inertia response of synchronous generator and PS-NE, and the inertia model of PS-NE considering with high renewable penetrations. Then the relation between inertia and frequency of PS-NE with high renewable penetrations are explored in this paper. Besides, the real-time inertia of the PS-NE is estimated by using the statistical algorithm based on the historical data of the PS-NE, and the range of the inertia and synchronous generator start-up capacity of the PS-NE can be estimated by using the statistical algorithm based on the renewable penetrations when we cannot obtain the historical data of the PS-NE. In addition, IEEE39 system of PS-NE is used to verify the relation between inertia and frequency of PS-NE with high renewable penetrations, and the inertia estimation of PS-NE with high renewable penetrations is analyzed in this paper.

Parameter identification of photovoltaic discrete-time equivalent model using the bat algorithm

As the development of photovoltaic (PV) power generation continuously accelerated the total installed capacity of PV, the traditional static load model is difficult to meet the needs of the power grid with the increasing penetration of PV. And the dynamic model of grid-connected PV power generation is complicated and there are plenty parameters need to be identified, so the dynamic model of grid-connected PV power generation are extremely challenging to apply in wide-area power system. In this paper, a discrete-time equivalent model of PV (PDEM) is established based on the third-order dynamic differential equation of the PV power generation system and the parameters of the PDEM are identified using the least squares (LS) and the bat algorithm (BA). Besides, the dynamic characteristics of the PV power generation grid connected system with different permeability and the fitting residuals of the two methods is analyzed in the IEEE14-bus system incorporated into the PV system. The applicability of the PDEM is verified by setting short circuit grounding fault and changing the PV permeability and voltage dip. The simulation results demonstrate that the PDEM has a strong adaptability and good applicability in the case of high PV permeability with a wide application. And the applicability of the BA in identification of PDEM are given in this paper.

A Method for Variance-Based Sensitivity Analysis of Cascading Failures

Cascading failures of relay operations in power systems are inherently linked with the propagation of wide-area power system blackouts. In this paper, we consider a power system cascading failure as an indicator matrix encoding: what power system relays operated within a cascading failure inherently capturing the component and the sequence of tripping events. We propose that this matrix may then be used with extended forms of variance-based sensitivity estimators to quantitatively rank how sensitive observed power system cascading failures are to power system variables, considering overall system cascading failures as well as cascading failures grouped by network area and relay types. We demonstrate our proposed method by investigating the sensitivity of cascading failures to relay parameters, system conditions, and fault location using a version of the IEEE 39 bus model modified to include protection relays, wind farms, and tap-changing transformers. Input power system variables included: system operational scenario, disturbance location, relay parameters or thresholds. The Case Studies' results confirm the method's utility by successfully generating relative rankings of input variables' importance with respect to cascading failure propagation. The results also show cascading failures' sensitivity to input variables to be high due to non-linear relationships between input variables and cascading failures.

Event-based model predictive damping control for power systems with cyber-attacks

This paper presents a secure model predictive damping control strategy for the wide-area power system (WAPS) under the DoS/deception attacks. Firstly, the model of WAPS under denial of service (DoS) attacks and deception attacks with the event-triggered mechanism (ETM) is established. Then, a wide-area model predictive damping controller is designed to damp out the interarea oscillations in the WAPS induced by cyber-attacks. Wide-area event-based model predictive damping control (WEMPDC) is proposed, by employing the wide-area information, the ETM is utilized to determine WEMPDC to defend against the cyber-attacks, in the meantime input-to-state stable in probability (ISSiP) of the closed-loop system is guaranteed. Furthermore, the validity of the WEMPDC is investigated by two-area four-machine and 16-machine 68-bus system.

A novel data-driven time-delay attack evaluation method for wide-area cyber–physical smart grid systems

One of the main challenges in information and communication technology, as well as wide-area power system researches, is the inability to stabilize the voltage and frequency for cyber–physical power systems under the communication time-delay. Thus, it is imperative to investigate the stochastic characteristics and quantitative method of time-delay. In this paper, a novel data-driven time-delay evaluation method for cyber–physical smart grid systems is proposed. The cyber layer and physical components in wide-area power systems are modeled. The transmitted process of the data packet in a cyber layer is considered, and the probability distribution function of multiple latencies in the spatial sequence is depicted by using M/M/1 queuing theory and signal convolution methods. The Sklar’s theorem and Copula’s theory are utilized to integrate distribution functions of multiple delays into a novel delay quantitative model. Furthermore, the time delay is divided according to the reason of its generation as two parts: (1) inherent delay by nature, and (2) delay caused by network attacks. Various sizes of delays caused by cyber-attacks are detected by using communication principle and likelihood ratio. Finally, the case studies under various scenarios are carried out on the New England ten-machine power system. The simulation results show that the stochastic latencies obey the proposed delay distribution model with probability 99.9%. The comparative results illustrate the mean-square error between the proposed and actual delay distribution probability function as 10 −4 . The delay caused by cyber-attacks can be separated from nature latency with the threshold η = 0 . 0101 .

Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system

<p>In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.</p>

Gaussian quantum particle swarm optimization-based wide-area power system stabilizer for damping inter-area oscillations

PurposeThe extensive increase in power demand has challenged the ability of power systems to deal with small-signal oscillations such as inter-area oscillations, which occur under unseen operating conditions. A wide-area measurement system with a phasor measurement unit (PMU) in the power network enhances the observability of the power grid under a wide range of operating conditions. This paper aims to propose a wide-area power system stabilizer (WAPSS) based on Gaussian quantum particle swarm optimization (GQPSO) using the wide-area signals from a PMU to handle the inter-area oscillations in the system with a higher degree of controllability.Design/methodology/approachIn the design of the wide-area stabilizer, a dead band is introduced to mitigate the influence of ambient signal frequency fluctuations. The location and the input signal of the wide-area stabilizer are selected using the participation factor and controllability index calculations. An improved particle swarm optimization (PSO) technique, namely, GQPSO, is used to optimize the variables of the WAPSS to move the unstable inter-area modes to a stable region in the s-plane, thereby improving the overall system stability.FindingsThe proposed GQPSO-based WAPSS is compared with the PSO-based WAPSS, genetic algorithm-based WAPSS and power system stabilizer. Eigenvalue analysis, time-domain simulation responses and performance index analysis are used to assess performance. The various evaluation techniques show that GQPSO WAPSS has a consistently good performance, with a higher damping ratio, faster convergence with fewer oscillations and a minimum error in the performance index analysis, indicating a more stable system with effective oscillation damping.Originality/valueThis paper proposes an optimally tuned design for the WAPSS with a wide-area input along with a dead-band structure for damping the inter-area oscillations. Tie line power is used as the input to the WAPSS and optimal tuning of the WAPSS is performed using an improved PSO algorithm, known as Gaussian quantum PSO.

Risk-Constrained Minimization of Combined Event Detection and Decision Time for Online Transient Stability Assessment

This paper addresses the problem of `quickest possible' online transient stability assessment, by minimizing the decision time of combined event detection that might lead to a system split and unstable generator group prediction, from real-time wide area power system measurements. More importantly it does so by respecting predefined probabilistic error constraints for the prediction. The statistical theory of optimal detection is applied, firstly to choose the detection threshold and secondly to select a flexible assessment time, after using probabilistic neural networks to provide a temporal representation of the data. On simulated wide area measurements from the interconnected New England test system and New York power system this approach is between two and three times faster on average than strategies based on fixed assessment times, despite having comparable error rates.

Application of predictive Wide-Area Power System Stabilisers on Distributed Generators in power systems with high penetration of renewable energy sources

Application of predictive Wide-Area Power System Stabilisers on Distributed Generators in power systems with high penetration of renewable energy sources

A multi-objective optimization model for robust tuning of wide-area PSSs for enhancement and control of power system angular stability

A multi-objective optimization model for robust tuning of wide-area power system stabilizers (WAPSSs) aiming the improvement and control of angular stability of electric power system (EPS) is presented in this paper. In this work the WAPSSs are referred to power system stabilizers (PSSs) of fixed-structure with insertion of remote signals, from phasor measurement units (PMUs), in the control loops, that are used in a wide-area control system (WACS). The proposed model is composed of objective functions to maximize both the small-disturbance angular stability (SDAS) through the damping ratio and the large-disturbance angular stability (LDAS) using an index derived from transient energy function (TEF). Moreover, three objectives functions that contribute to the previous ones are taken into account: maximization of power system robustness for several operating conditions, minimization of eigenvalues sensitivity to small disturbances, and minimization of eigenvalues sensitivity with respect to time delay variations of remote signals. Swarm intelligence-based techniques are used to solve the multi-objective optimization model. Simulation results, with the benchmark interconnected system New England test system – New York power system (NETS-NYPS) of 16-generator and 68-bus, corroborate the applicability of the proposal.

Dynamic state estimation of power system with stochastic delay based on neural network

With the development of the wide-area power systems, time delays, including inherent and malicious network attacks time delays, are introduced into control signal communication channel, which jeopardize power system operation, control and protection. To eliminate the threats, state estimation was used to monitor the power system operating state. However, traditional state estimation methods are difficult to carry out due to the time delays have randomness. Therefore, a dynamic state estimation (DSE) method for power system based on delay and its stochastic characteristics was proposed in this paper. First, dynamic equations of important components, such as generators, exciters and power system stabilizers (PSSs), are introduced to describe complex power systems. In order to facilitate the use of Kalman filter (KF) methods, the power system state equations are discretized. Then, the correlation between stochastic delays and power system operating state indicated by neural network (NN). On the basis of the correlation, a KF method combined with NN has been proposed to adapt to the power system with stochastic delays. Finally, to verify the effectiveness of the proposed method, simulation experiments has been carried out on an actual power system. The simulation results show that the proposed method performs well in monitoring generators’ dynamic performance when the power system was disturbed by stochastic delays.

심층 신경망을 이용한 재생에너지원의 PMU 빅 데이터기반 새로운 계통 현상 판별

Recently, for real-time monitoring of renewable energy, a wide area power system monitoring and operation technology using PMU has emerged. Through WAMS based on the PMU, time synchronization data for a wide area is acquired and a vast amount of data is accumulated. Therefore, it is a problem to be solved in the future to process this data and deliver highly usable and valuable system status information to system operators. This paper proposes a new systemic phenomenon identification algorithm using big data of PMU installed in RES and a DNN. First, the PMU installed at the RES in the Gangwon region is introduced, and then the data structure collected is explained. Next, by analyzing each system phenomenon from the PMU data, a total of 8 types of system data such as steady state, tap rise, tap fall, feed-in and out, etc. are generated. After conducting supervised learning by constructing learning data for 8 systematic phenomena using a DNN, systematic phenomena discrimination is performed on the DNN model learned through the test data. Finally, the algorithm was designed, implemented, and evaluated to identify robust systematic phenomena for new PMU based big data. The simulation results showed that the proposed new algorithm accurately discriminates all systematic phenomena.

Wide-Area Damping Control Resilience Towards Cyber-Attacks: A Dynamic Loop Approach

By increasingly relying on network-based operation for control, monitoring, and protection functionalities, modern wide-area power systems have also become vulnerable to cyber-attacks aiming to damage system performance and/or stability. Resilience in state-of-the-art methods mostly relies on known characteristics of the attacks and static control loops (i.e., with fixed input/output channels). This work proposes a `dynamic loop' wide-area damping strategy, where input/output channel pairs are changed dynamically. We study `reactive' dynamic switching in case of detectable attack and `pro-active' dynamical switching, in case of undetectable (stealth) attacks. Stability of the dynamic loop is presented via Lyapunov theory, under parametric perturbations, average dwell time switching and external perturbations. Using two- and five-area IEEE benchmarks, it is shown that the proposed strategy provides effective damping and robustness under various detectable (e.g., false data injection, denial-of-service) and stealth (replay, bias injection) attacks.

Improved Design of Interval Type-2 Fuzzy based Wide Area Power System Stabilizer for Inter-area Oscillation Damping

The interconnection of power system due to the ever-time increase in power demand has caused inter-area oscillations as a challenge to power system stability. The present paper proposes the novel design of an Interval Type-2 Fuzzy based Wide Area Power System Stabilizer to damp the inter-area mode of oscillations for improving the power system stability. The usage of Wide Area Measurement Systems for continuous monitoring of the power grid plays a significant role in maintaining the stability of the power grid. The proposed controller uses wide-area signals from WAMS as the input signals. The controllability index calculation performs the selection of the most affected wide-area signals. The participation factor is used to identify the location of the controller. Sliding Surface approach is introduced in the controller to upgrade the performance of the controller during different operating conditions. The sliding surface approach has made the system insensitive to the parameter variations. The interval type-2 fuzzy control is a model-free approach with better control performance, due to its higher degree of freedom of interval type-2 fuzzy sets. Finally, the optimal tuning of sliding surface parameters has been considered as an optimization problem with the minimization of Integral Time Square Error using Real Coded Genetic Algorithm to enhance the damping control. The proposed Fuzzy controller has been tested in two area, four machine, 11 bus IEEE benchmark system. From the simulation responses and the comparison with different controllers, the proposed controller shows robustness and effectiveness with more accurate dynamic response and better damping of inter-area oscillations under different system operating conditions and load perturbations.

Wide area power system transient stability prediction incorporating dynamic capability curve and generator bus coherency

Any disturbance in the grid affects the real and reactive power outputs of the generators. The capability of producing real and reactive power is usually known as capability curve. The conventional static capability uses predefined operating constraints pertaining to mechanical power, rotor angle, terminal voltage, rated power factor and field voltage of the generator. However, the generators connected to the grid have dynamic operating states which can be predicted in real-time using data from phasor measurement unit (PMU). The generator transient stability is important from power system stability point of view because tripping of generators due to instability creates cascading effects. The present work proposes the real-time monitoring of dynamic state of generators through dynamic capability curve using rotor angle. The rotor angles are not directly measured by the PMUs and hence are being estimated using extended Kalman filter. Values of rotor angle are used for principal component analysis (PCA) for identifying coherent and non-coherent generators. Any non-coherent generator beyond the dynamic capability curve limits is a clear indication of eventually becoming an unstable generator and has been termed as most critical generator. The proposed scheme employs multiple artificial neural network to incorporate inference from dynamic capability curve as well as that from PCA for identifying critical generators as well as predicting the degree of criticality of marginally critical generators within a time window of 12 cycles after occurrence of fault. The simulation results using case studies from IEEE-39 bus system validates the efficacy of the proposed methodology.

Performance of wide-area power system stabilizers during major system upsets: investigation and proposal of solutions

© 2021 Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of Benasla, M., Denai, M., Liang, J. et al. Performance of wide-area power system stabilizers during major system upsets: investigation and proposal of solutions. Electr Eng (2021). The final authenticated version is available online https://doi.org/10.1007/s00202-020-01168-3

Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network weights are updated based on real-time data from Phasor measurement units. Resilience assessment based on failure probability, financial impact, and time-series data in grid failure management determine the norm H2. To demonstrate the effectiveness of the proposed framework, a time-domain simulation case study based on the IEEE-39 bus system was performed. For a one-channel attack on the test system, the resiliency index increased to 0.962, and inter-area damping ξ was reduced to 0.005. The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well. Results also offer robust management of power system resilience and timely control of the operating conditions.