A Unified Online Assessment Framework for Pre-Fault and Post-Fault Dynamic Security

With the continuous expansion of the power system scale and extensive application of phasor measurement units (PMUs), the secure operation of power systems has been increasingly concerned. In this paper, an integrated scheme for online dynamic security assessment (DSA) based on feature selection and regression prediction is proposed. First, partial mutual information (PMI) and the Pearson correlation coefficient (PCC) are used to select the key variables in the feature selection process. Second, an iterated random forest (IRF) is applied to predict the transient stability margin (TSM) based on the selected variables. Combining the feature selection process and regression prediction, a DSA model is constructed. Finally, a spatial-temporal dynamic visualization approach is proposed, which can intuitively provide real-time dynamic security information of power systems. The integrated scheme, which is tested on the IEEE 39-bus system and a practical 1648-bus system provided by the software PSS/E, exhibits desirable assessment accuracy and is suitable for online application. In the robustness test, some impact factors for power system operation are considered, such as topology change, variation of generator/load power distribution and variation of load characteristics. Moreover, the impacts of missing data and measurement noise are studied.

With the wide interconnection of power systems and extensive application of phasor measurement units (PMUs), the secure operation of power systems is facing considerable challenges. To satisfy the demand of online dynamic security assessment (DSA) for modern power systems, a data-driven scheme based on sparse projection oblique randomer forests (SPORF) is proposed, which includes offline training, periodic update and online assessment. In the first stage, an improved adaptive synthetic sampling (ADASYN) method is developed to mitigate the class imbalance problem for the data-driven DSA approach. Then, the SPORF-based DSA model is trained using crucial features with low redundancy selected by a feature selection procedure based on the minimal-redundancy-maximal-relevance (MRMR) criterion. In the second stage, the periodic update of the DSA model for unseen system topologies is executed to enhance the robustness of the model. In the third stage, the trained model can provide the DSA result immediately when the real-time operation information of a system is received. The satisfactory performance of the proposed scheme is demonstrated through a series of tests and the comparisons on a 23-bus system and a practical 1648-bus system.

A data-driven approach for online dynamic security assessment with spatial-temporal dynamic visualization using random bits forest

Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.

An integrated scheme for dynamic security assessment considering misclassification constraint based on umbrella Neyman-Pearson classifiers

A new dynamic security assessment framework based on semi-supervised learning and data editing

info:eu-repo/semantics/published

In India Power system monitoring is based mainly on measurements provided by Remote Terminal Unit (RTU). For power system measurement synchronized phasor or Phasor Measurement Unit (PMU) is popular choice in abroad and its popularity is increasing day by day because of its several feature. Synchrophasor technology was first introduced in india in 2011-12[7],[8],[9]. Direct measurement of voltage or current phasor is possible because of PMU which was not possible earlier. Global positioning system (GPS) provides time synchronized data. In this paper a new approach is used for optimal placement of PMU, counting their number & time elapsed. The approach is applied on IEEE-14 bus system bus system .Here all the bus which are taken into consideration is either completely observable or at some depth of un-observability is also taken into account. Index Terms— PMU, Observability, Phasor, IEEE-14 Bus system

Security-constrained optimal placement of PMUs using Crow Search Algorithm

BackgroundNew South Wales (NSW), Australia has a network of multirole retrieval physician staffed helicopter emergency medical services (HEMS) with seven bases servicing a jurisdiction with population concentrated along the eastern seaboard. The aim of this study was to estimate optimal HEMS base locations within NSW using advanced mathematical modelling techniques.MethodsWe used high resolution census population data for NSW from 2011 which divides the state into areas containing 200–800 people. Optimal HEMS base locations were estimated using the maximal covering location problem facility location optimization model and the average response time model, exploring the number of bases needed to cover various fractions of the population for a 45 min response time threshold or minimizing the overall average response time to all persons, both in green field scenarios and conditioning on the current base structure. We also developed a hybrid mathematical model where average response time was optimised based on minimum population coverage thresholds.ResultsSeven bases could cover 98% of the population within 45mins when optimised for coverage or reach the entire population of the state within an average of 21mins if optimised for response time. Given the existing bases, adding two bases could either increase the 45 min coverage from 91% to 97% or decrease the average response time from 21mins to 19mins. Adding a single specialist prehospital rapid response HEMS to the area of greatest population concentration decreased the average state wide response time by 4mins. The optimum seven base hybrid model that was able to cover 97.75% of the population within 45mins, and all of the population in an average response time of 18 mins included the rapid response HEMS model.ConclusionsHEMS base locations can be optimised based on either percentage of the population covered, or average response time to the entire population. We have also demonstrated a hybrid technique that optimizes response time for a given number of bases and minimum defined threshold of population coverage. Addition of specialized rapid response HEMS services to a system of multirole retrieval HEMS may reduce overall average response times by improving access in large urban areas.

With the development of synchronised measurement technique, online dynamic security assessment (DSA) is of great significance to prevent power system blackout. Recently, based on the phasor measurement unit (PMU) data, intelligent data‐driven techniques have been rapidly developed for online DSA owing to their fast decision speed, less data requirement, and decision rule discovery ability. The interpretable decision rules provide useful information for preventive control and post‐event auditing. However, in case of incomplete measurement events, such as PMU failure, communication loss, and phasor data concentrator failure, the accuracy and the transparency of the intelligent models can be significantly impaired by such incomplete data. To overcome this problem, this paper proposes a robust white‐box model that can sustain DSA accuracy and survive the model interpretability and transparency against incomplete PMU data. The proposed model is tested on New England 39‐bus system and demonstrates higher robustness over existing methods.

Electrical coordinate based on impedance electrical distance is an effective feature expression method of fault location. Applied to data-driven dynamic security assessment, electrical coordinate can improve dynamic security assessment accuracy significantly. Based on the concept of the electrical coordinate system, an expansion method of the electrical coordinate system is proposed, which further improves the accuracy of dynamic security assessment. First, the reasons why the representation accuracy of the electrical coordinate system is low in partial areas are analyzed. The blind point, blind line, and blind area for fault location feature expression are defined. Then, by analyzing the electrical relationship between two points, the judgment criterion of blind lines is determined, and the rapid searching method of blind lines is proposed. Finally, the identification and fine-tuned algorithm of blind area is proposed. The electrical coordinate system is expanded by supplementing reference nodes. And the framework of data-driven dynamic security assessment based on expanded electrical coordinate system is constructed. Taking a provincial power grid as the example, the applicability of the expanded electrical coordinate system in the dynamic security assessment is verified.

A hybrid simulation/measurement-based framework for online dynamic security assessment (DSA) is proposed in this work. It combines the strengths and features of simulation-based and measurement-based approaches to develop a tool that integrates the results and provides real-time situational awareness on available operating margins against major stability problems. High performance computing capability is suggested and used in the simulation-based engine, while synchrophasor measurements are used as the input to the measurement-based stability assessment algorithms. The proposed framework is expected to provide solid foundation for new generation of real-time DSA tools that are needed for operators to assess in real-time the system's dynamic performance and operational security risk.

A causality based feature selection approach for data-driven dynamic security assessment

With widely deployment of phasor measurement units (PMU), data-driven dynamic security assessment (DSA) has been a promising tool to help power system counteract large disturbance and avoid instability. In case of PMU lost events, the DSA performance may be dramatically degraded. This paper designs a new online DSA model based on hybrid ensemble learning to address the missing data problem. In this method, a set of missing data estimators is proposed to restore the system measurement, and potential inaccurately estimated features are detected in feature validation stage to further ensure the model performance. After excluding inaccurate estimations, the filled-up dataset is provided to support accurate DSA under PMU lost situation. A hybrid ensemble of extreme learning machine (ELM) and random vector functional link networks (RVFL) is designed as learning engine for estimators and DSA classifiers. Simulation results show this approach achieves low estimation error as well as high DSA accuracy and strong robustness.