KNOWLEDGE-BASED NEURAL NETWORK FOR LINE FLOW CONTINGENCY SELECTION AND RANKING

The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution

Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks

A number of contingencies simulated during dynamic security assessment do not generate unacceptable values of power system state variables, due to their small influence on system operation. Their exclusion from the set of contingencies to be simulated in the security assessment would achieve a significant reduction in computation time. This paper defines a critical contingencies selection method for on-line dynamic security assessment. The selection method results from an off-line dynamical analysis, which covers typical scenarios and also covers various related aspects like frequency, voltage, and angle analyses among others. Indexes measured over these typical scenarios are used to train neural networks, capable of performing on-line estimation of a critical contingencies list according to the system state.Fil: Schweickardt, Gustavo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Fundación Bariloche. Instituto de Economía Energetica; ArgentinaFil: Gimenez Alvarez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; Argentin

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton-Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.Comment: Accepted by Applied Science

Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies

Probabilistic Performance Index based Contingency Screening for Composite Power System Reliability Evaluation

Composite power system reliability involves assessing the adequacy of generation and transmission system to meet the demand at major system load points. Contingency selection was being the most tedious step in reliability evaluation of large electric systems. Contingency in power system might be a possible event in future which was not predicted with certainty in earlier research. Therefore, uncertainty may be inevitable in power system operation. Deterministic indices may not guarantee the randomness in reliability assessment. In order to account for volatility in contingencies, a new performance index proposed in the current research. Proposed method assimilates the uncertainty in computational procedure. Reliability test systems like Roy Billinton Test System-6 bus system and IEEE-24 bus reliability test systems were used to test the effectiveness of a proposed method

Model-based and Model-free Approaches for Power System Security Assessment

Continuous security assessment of a power system is necessary to insure a reliable, stable, and continuous supply of electrical power to customers. To this end, this dissertation identifies and explores some of the various challenges encountered in the field of power system security assessment. Accordingly, several model-based and/or model-free approaches were developed to overcome these challenges. First, a voltage stability index, named TAVSI, is proposed. This index has three important features: TAVSI applies to general load models including ZIP, exponential, and induction motor loads; TAVSI can be used for both measurement-based and model-based voltage stability assessment; and finally, TAVSI is calculated based on normalized sensitivities which enables identification of weak buses and the definition of a global instability threshold. TAVSI was tested on both the IEEE 14-bus and the 181-bus WECC systems. Results show that TAVSI gives a reliable assessment of system stability. Second, a data-driven and model-based hybrid reinforcement learning approach is proposed for training a control agent to re-dispatch generators’ output power in order to relieve stressed branches. For large power systems, the agent’s action space is highly dimensioned which challenges the successful training of data-driven agents. Therefore, we propose a hybrid approach where model-based actions are utilized to help the agent learn an optimal control policy. The proposed approach was tested and compared to the generic data-driven DDPG-based approach on the IEEE 118-bus system and a larger 2749-bus real-world system. Results show that the hybrid approach performs well for large power systems and that it is superior to the DDPG-based approach. Finally, a Convolutional Neural Network (CNN) based approach is proposed as a faster alternative to the classical AC power flow-based contingency screening. The proposed approach is investigated on both the IEEE 118-bus system and the Texas 2000-bus synthetic system. For such large systems, the implementation of the proposed approach came with several challenges, such as computational burden, learning from imbalanced datasets, and performance evaluation of trained models. Accordingly, this work contributes a set of novel techniques and best practices that enables both efficient and successful implementation of CNN-based multi-contingency classifiers for large power systems

Efficient processing of system scenarios in statistical and machine learning studies for power system operational and investment planning

Power System security assessment and the associated planning studies are becoming more and more complex with ever increasing uncertainties in all time horizons. An effective means of performing operational and investment planning studies of network limitations associated with static or dynamic post-disturbance performance problems has been to take a Monte Carlo simulation based approach. The approach harnesses computing power to develop a database of post-contingency response over a wide range of different operating conditions, and then apply statistical or machine learning methods to extract useful planning and operational information from the database. Key to the machine learning based planning approach is the manner in which the different operating conditions are sampled to generate a training database. This work develops an efficient sampling procedure that maximizes information content in the training database while minimizing computing requirements to generate it, by finding the most influential region in the sampling state space and sampling operating conditions from it according to their relative likelihood. The Monte-Carlo variance-reduction methods are used to construct the proposed sampling approach, which is envisioned to allow market-oriented industries to operate the system according to economic rule. The dissertation also develops a comprehensive methodology to perform decision tree based security assessment for multiple contingencies. The system security limits and associated operating rules depend on the set of contingencies considered for planning. Considering the probabilistic nature of the power system, this work develops a risk based contingency ranking method that helps in screening the most critical contingencies from a contingency list. The developed contingency risk estimation method gives realistic risk indices since it takes into account the non-parametric nature of operating condition distribution, and it also saves tremendous computational cost since it uses linear sensitivities to estimate the risk. Finally, a contingency grouping method is proposed that guides in generating common operating rules for every group that performs well for all the contingencies in that respective group, thereby providing system operators the benefit of dealing with lesser number of rules. The contingency grouping is based on newly devised metric called progressive entropy that helps in finding similarities among contingencies based on their consequences on the operating conditions along all the load ranges, and not just their proximity in the grid. The proposed methods are implemented in the west France, Brittany region of RTE-France\u27s test system to derive decision rules for multiple contingencies against voltage stability problems

Fast dynamic voltage security marginestimation: concept and development

This study develops a machine learning-based method for a fast estimation of the dynamic voltage security margin(DVSM). The DVSM can incorporate the dynamic system response following a disturbance and it generally provides a bettermeasure of security than the more commonly used static voltage security margin (VSM). Using the concept of transient P - Vcurves, this study first establishes and visualises the circumstances when the DVSM is to prefer the static VSM. To overcomethe computational difficulties in estimating the DVSM, this study proposes a method based on training two separate neuralnetworks on a data set composed of combinations of different operating conditions and contingency scenarios generated usingtime-domain simulations. The trained neural networks are used to improve the search algorithm and significantly increase thecomputational efficiency in estimating the DVSM. The machine learning-based approach is thus applied to support theestimation of the DVSM, while the actual margin is validated using time-domain simulations. The proposed method was testedon the Nordic32 test system and the number of time-domain simulations was possible to reduce with ∼70%, allowing systemoperators to perform the estimations in near real-time

New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design

Graph Convolutional Networks for probabilistic power system operational planning

Probabilistic operational planning of power systems usually requires computationally intensive and time consuming simulations. The method presented in this paper provides a time efficient alternative to predict the socio-economic cost of system operational strategies using graph convolutional networks. It is intended for fast screening of operational strategies for the purpose of operational planning. It can also be used as a proxy for operational planning that can be used in long term development studies. The performance of the model is demonstrated on a network inspired by the Nordic power system.Graph Convolutional Networks for probabilistic power system operational planningacceptedVersio