Real-time Dynamic Security Assessment Research Articles

A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems

• A hybrid model is developed, blending ELM and Levenberg-Marquardt. • Particle swarm optimization-based weighted averaging ensemble is developed. • Energy margin and time margin are calculated for better application of techniques. • Hybrid ELM is found most suited technique for online DSA application. This manuscript develops a new hybrid-extreme learning machine (ELM) based ensemble model for real-time dynamic security assessment (DSA) of power systems. In order to boost the forecasting accuracy of ELM algorithm, a Levenberg-Marquardt (LM) backpropagation algorithm is used. The Ensemble strategy takes advantage of complementary information to improve the generalization capability of model. To realize an accurate ensemble model, particle swarm optimization (PSO) based weighted averaging is used to combine the individual forecasts. Transient energy function (TEF) terms, generator real and reactive power outputs and fault location are taken as input features for classification as well as prediction of transient energy margin (TEM) and time margin (TM) for a given operating condition and fault location. The potential of the proposed technique is validated on New England 39-bus and 68-bus test power systems. The results demonstrate that the developed model outperforms other state-of-the-art methods.

Robustness Verification for Machine-Learning-Based Power System Dynamic Security Assessment Models Under Adversarial Examples

Based on machine learning (ML) technique, the data-driven power system dynamic security assessment (DSA) has received significant research interest. Yet, the well-trained ML-based models with high training and testing accuracy may be vulnerable to the adversarial example, which is a modified version of the original sample that is intentionally perturbed but retains being very close to the original one. Such adversarial examples can mislead the DSA results and lead to catastrophic consequences. Thus, the accuracy index alone is not enough to represent the performance of the ML-based DSA models. To evaluate the ML-based DSA models and provide formal robustness guarantee for real-time DSA, this article proposes an adversarial robustness verification method to quantify the ability of ML-based DSA models against all kinds of adversarial examples. A model-free and attack-independent robust index is defined for both differentiable and nondifferentiable attack scenarios. Simulation results have verified the effectiveness of the proposed adversarial robustness verification method and the superiority of robust index compared with the upper bound of the adversarial perturbations computed by existing adversarial attack methods.

Verifying Machine Learning conclusions for securing Low Inertia systems

Machine Learning (ML) for real-time Dynamic Security Assessment (DSA) promises a probabilistic approach to secure lower safety margins and costs. However, future systems with a high share of renewables have low inertia and converter-interfaced devices resulting in faster dynamics. Past research on ML-based DSA used high inertia systems to study ‘the best’ ML data, features, and models building upon each other’s work for decades. Seldom has ML-based research for DSA questioned whether the underlying assumptions for (and the conclusions of) these studies are still valid for low inertia systems. This work studies exemplary changes in assumptions (and conclusions) for ML-based DSA when moving from High Inertia (HI) to Low Inertia (LI) systems. The dynamical system of the LI system is brought in perspective with the most typical ML-based approaches, which are organised in sequential steps. The steps consider the generation of the training database, the data pre-processing and feature selection, the model training and validation. This work analyses each step individually for the changed assumptions in the dynamical LI system, and subsequently, a case study provides the evidence that considering a LI system to identify the ‘best’ ML approaches is important. The case studies on IEEE 14 and 68 bus systems confirm that LI systems must be optimised for security (otherwise, they result in 80% less security than HI systems). The key findings, however, are that using ML makes significantly more sense in LI systems than in HI systems as the LI dynamics are in shorter timescales (and the advantage of ML is to predict security in milliseconds) and that secure/insecure operations can be separated more straightforwardly in LI systems as ML increases the accuracy by up-to 40% towards close to 100% when using neural networks. • A dynamic model for low inertia power systems. • Impact analysis of the newly introduced low-inertia dynamics on ML-based real-time dynamic security assessment. • Case studies on the IEEE 14 and 68 bus system to compare the performance of ML-based approaches in high and low inertia systems. • High performance of ML-based security in low inertia systems.

Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System

In the smart grid paradigm, growing integration of large-scale intermittent renewable energies has introduced significant uncertainties to the operations of an electric power system. This makes real-time dynamic security assessment (DSA) a necessity to enable enhanced situational-awareness against the risk of blackouts. Conventional DSA methods are mainly based on the time-domain simulation, which are insufficiently fast and knowledge-poor. In recent years, the intelligent system (IS) strategy has been identified as a promising approach to facilitate real-time DSA. While previous works mainly concentrate on the rotor angle stability, this paper focuses on another yet increasingly important dynamic insecurity phenomenon-the short-term voltage instability, which involves fast and complex load dynamics. The problem is modeled as a classification subproblem for transient voltage collapse and a prediction subproblem for unacceptable dynamic voltage deviation. A hierarchical IS is developed to address the two subproblems sequentially. The IS is based on ensemble learning of random-weights neural networks and is implemented in an offline training, a real-time application, and an online updating pattern. The simulation results on the New England 39-bus system verify its superiority in both learning speed and accuracy over some state-of-the-art learning algorithms.

Real-time dynamic security assessment of power grids

This paper presents a dynamic security assessment solution, which can be used in the power system control room to improve system stability. It is based on a set of security indices. The indices are able of establishing contingencies’ severity levels as a measure of different aspects of power system security. A system based on fuzzy logic is used to combine the indices into a single composite index. The composite index is able to alert the control operator to the network conditions that represent a significant risk to system security based on over-all system performance.

An Intelligent Dynamic Security Assessment Framework for Power Systems With Wind Power

The increasing penetration of wind power can alter the dynamic security characteristic of a power system. To accommodate rapid and volatile wind power variations, dynamic security assessment (DSA) against foreseeable disturbances is required to be carried out online and provide security monitoring results within sufficiently small time frame. Based on soft computing (SC) technologies, this paper develops an intelligent framework for real-time DSA of power systems with large penetration of wind power. It consists of a DSA engine whose role is to perform real-time DSA of the power system, a wind power and load demand (W&LF) forecasting engine for offline and online predicting wind power generation and electricity load demand, a database generation (DBG) engine for generating instances to train the DSA engine, and a model updating (MU) engine for online updating the DSA engine. Case studies are conducted on two benchmark systems where high DSA efficiency and accuracy are obtained. This framework can be an ideal candidate for advanced security monitoring in the future SmartGrid control centres.

A Reliable Intelligent System for Real-Time Dynamic Security Assessment of Power Systems

A new intelligent system (IS) is developed for real-time dynamic security assessment (DSA) of power systems. Taking an ensemble learning scheme, the IS structures a series of extreme learning machines (ELMs) and generalizes the randomness of single ELMs during the training. Benefiting from the unique properties of ELM and the strategically designed decision-making rules, the IS learns and works very fast and can estimate the credibility of its DSA results, allowing an accurate and reliable pre-fault DSA mechanism: credible results can be directly adopted while incredible results are decided by alternative tools such as time-domain simulation. This makes the IS promising for practical application since the potential unreliable results can be eliminated for use. Case studies considering classification and prediction are, respectively, conducted on an IEEE 50-machine system and a dynamic equivalent system of a real-world large power grid. The efficiency, robustness, accuracy, and reliability of the IS are demonstrated. In particular, it is observed that the IS could provide 100% classification accuracy and very low prediction error on its decided instances.

Real-time dynamic security assessment: fast simulation and modeling applied to emergency outage security of the electric grid

A major drawback of current dynamic security assessment (DSA) software used to analyze the transient and dynamic responses to a large number of potential power system disturbances in a transient time frame is the long simulation time required to complete the calculations, making it impractical for real-time applications. This paper describes an attempt to dramatically reduce the time for DSA calculations so that DSA analyses can be converted from offline studies to routine, online use to aid grid operators in their real-time controller analyses. Using a new distributed computational architecture among other techniques, a modified version of DSA is developed which performs calculations fast enough for real-time applications to energy management systems (EMS) that operate today's grid systems.

Comparison of algorithms for transient stability simulations on shared and distributed memory multiprocessors

Parallel processing has been applied for time domain simulations of power system transient behavior in order to implement real-time dynamic security assessment. In this paper, two different algorithms have been implemented and compared: the Shifted-Picard (SP) and the Very DisHonest Newton (VDHN). The former has been proved to be effective when parallelism-in-time is adopted whereas the latter is an effective solver when parallelism-in-space is exploited. Furthermore, two different parallel computing architectures have been considered: namely, the Sequent Symmetry computer with 26 processors which is a data shared memory machine and the nCUBE characterized by 128 CPUs which is a typical message passing parallel machine. A realistic network with 662 buses has been used to assess the performance of the different implementations. The comparison of the results allows the reader to understand both the limitations of the algorithmic approaches and the constraints imposed by the two parallel architectures. An optimal grain of the parallelism associated to the problem can be identified through the reported experience.