Model-Free MLE Estimation for Online Rotor Angle Stability Assessment With PMU Data

Wei, Shaopan; Yang, Ming; Qi, Junjian; Wang, Jianhui; Ma, Shiying; Han, Xueshan

doi:10.1109/tpwrs.2017.2761598

Cited by 50 publications

(29 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The torque coefficients K s and K d can be calculated by solving Equation (7). Although the calculated values are accurate, the application of the LS method requires data for the entire swing [5,6]. The need for such large data collection also requires a long calculation time.…”

Section: Ls Methodsmentioning

confidence: 99%

See 1 more Smart Citation

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

et al. 2020

View full text Add to dashboard Cite

This study discusses the evaluation of oscillatory stability based on the synchronizing K s and damping K d torque coefficients for a single-machine system connected to an infinite bus (SMIB). Particle swarm optimization (PSO) technique is used to determine K s and K d values and subsequently identify the oscillatory stability conditions in the SMIB. The ability of PSO is compared with those of evolutionary programming (EP) techniques and artificial immune system (AIS). The least square (LS) method is selected as the benchmark for K s and K d values determined by PSO, EP, and AIS. Simulation results show that PSO successfully estimated K s and K d values closest to LS compared with EP and AIS. PSO also uses lower computational time compared with those of the two other techniques. The proposed technique is suitable for solving oscillatory stability problems based on the determination of eigenvalues and minimum damping ratio.Energies 2020, 13, 1231 2 of 15 K d values. Thus, heuristic techniques are introduced to solve this problem. K s and K d values will be optimized from the beginning of the data until constant K s and K d values are obtained. Therefore, only a portion of the data is necessary for estimating K s and K d . Minor data errors do not significantly affect the determination of K s and K d values.In recent years, the use of artificial intelligence (AI) technology has become the preferred option in solving power system problems. The use of AI is introduced to solve the optimum values of a system or condition particularly in the fields of economic dispatch, capacitor placement and sizing, and assessment and improvement of voltage and oscillatory stability. Artificial neural networks [16,17], evolutionary programming (EP) [18][19][20], artificial immune systems (AIS) [21][22][23], and ant colony optimization (ACO) [24][25][26] are AI approaches that are commonly used in power systems. The EP algorithm is modeled on the biological evolution process of solving a complex problem. The main features of EP include the mutation process of the next generation and the selection of increasingly powerful genes. The AIS algorithm uses a concept similar to that of EP. Although both concepts are biologically based on living things, EP focuses on the evolution of living things, whereas AIS adopts the concept of the living immune system. The difference between AIS and EP algorithms is that AIS has an additional process of cloning called the clonal selection algorithm. However, the ACO approach is inspired by the true behavior of ants while searching for food and interacting with fellow ants. In ACO, artificial ants (the search agent) will communicate by using pheromones, which guide the searcher ants to solve the calculation problem by tracking the best route. Meanwhile, the particle swarm optimization (PSO) [27][28][29][30] concept mimics the movements of a herd, such as the behavior of schooling fish and swarming insects. This technique was originally founded based on the population of random particles, in which every p...

show abstract

Section: Ls Methodsmentioning

confidence: 99%

“…Power generation companies always innovate to ensure that the energy supply is sufficient and stable. Accordingly, small signal stability is a frequently discussed topic, as reported in [1][2][3][4][5]. This system must be tracked online because its power system operating condition changes over time.…”

Section: Introductionmentioning

confidence: 99%

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Therefore, the typical dynamic characteristics of post-fault phase trajectories (δ-ω curves) for both GP and single machine infinite bus system are the same. Typical dynamic characteristics of post-fault δ-ω curves of G i -G j including four different stability conditions are shown in Fig.2 [30], in which the black spots denote the points of δ-ω curves at the fault-clearing time.…”

Section: Proposed Coherent Groups Identification Approach a Physmentioning

confidence: 99%

“…According to the typical dynamic characteristics of the post-fault phase trajectories of GP as shown in Fig. 2, five different swing types of post-fault angular velocity deviation curves can be identified [30]. Fig.…”

Section: ) Establishing the Mathematical Relationship Of The Lle Andmentioning

confidence: 99%

An WAMS-Based Online Generators Coherency Identification Approach for Controlled Islanding

et al. 2020

View full text Add to dashboard Cite

In this paper, an online model-free approach based on the largest Lyapunov exponents (LLE) and angular velocity deviation is provided for coherent groups identification (CGI). Firstly, by using a model-free LLE calculation method, the generator coherency identification (GCI) criterions are proposed by establishing the mathematical relationship between the LLE and angular velocity difference. Secondly, an online CGI scheme for power systems is designed. Compared with the existing measurement-based methods, the breakthrough work of this paper is that the proposed GCI criterions combines the stability theory of nonlinear dynamic trajectory based on LLE with the physical mechanism of the out-of-step between generators, such that it can improve both the speed and the accuracy of the GCI. Besides, the proposed approach needs not to find the optimal observation window size and shorten the required observation window size greatly. Moreover, it can be applied to online CGI with small computation requirement. Extensive test results on New England 39-Bus System and practical East China (EC) power grid, as well as the comparisons with existing CGI methods verify high accuracy and efficiency of the proposed approach. INDEX TERMS Coherency identification, largest Lyapunov exponents (LLE), dynamic features, post-fault phase trajectory, wide-area measurement system (WAMS).

show abstract

“…Nevertheless, the post‐fault stability status is still difficult to determine because the MLE may oscillate from positive and negative values before the post‐fault system settles down. To address this problem, a recursive least‐squares‐based method is proposed in [9] for fast estimating the MLE, and therefore the monitoring window is shortened. So far, the connection between the change sign of MLE and the system's passing through the CUEP has not been studied.…”

Section: Introductionmentioning

confidence: 99%

Non‐parametric statistics‐based predictor enabling online transient stability assessment

Liu

et al. 2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.

show abstract

Model-Free MLE Estimation for Online Rotor Angle Stability Assessment With PMU Data

Cited by 50 publications

References 56 publications

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

An WAMS-Based Online Generators Coherency Identification Approach for Controlled Islanding

Non‐parametric statistics‐based predictor enabling online transient stability assessment

Contact Info

Product

Resources

About