Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

Zhang, Yanjun; Li, Tie; Na, Guangyu; Li, Guoqing; Li, Yang

doi:10.1155/2015/529724

Cited by 55 publications

(46 citation statements)

References 32 publications

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“…3. The network of 10 datacenters on IEEE 39-bus test system [43]. The total power demand is 14 GW, and the total datacenter power demand is 250 MW.…”

Section: Stabilitymentioning

confidence: 99%

Geographically Coordinated Primary Frequency Control

Comden

Zhao

et al. 2019

IEEE Trans. Control Netw. Syst.

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Primary Frequency Control (PFC) is a fast acting mechanism used to ensure high-quality power for the grid that is becoming an increasingly attractive option for load participation. Due to speed requirement and other considerations, it is often desirable to have distributed control laws. Current distributed PFC designs assume that the costs at each geographic location are independent. However, many networked systems, such as those for cloud computing, have interdependent costs across locations and therefore need geographic coordination. In this paper, distributed control laws are designed for interdependent, geo-distributed loads in PFC based on the optimality conditions of the global system. The controlled frequencies are provably stable, and the final equilibrium point is proven to strike an optimal balance between load participation and the frequency's deviation from its nominal set point. We evaluate the proposed control laws with realistic numerical simulations. Under current technology, the proposed control laws achieve a convergence time that is smaller than droop control alone and is comparable to that of distributed control without interdependent costs. Results also highlight significant cost savings over existing approaches under a variety of settings.

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“…3. The network of 10 datacenters on IEEE 39-bus test system [43]. The total power demand is 14 GW, and the total datacenter power demand is 250 MW.…”

Section: Stabilitymentioning

confidence: 99%

Geographically Coordinated Primary Frequency Control

Comden

Zhao

et al. 2019

IEEE Trans. Control Netw. Syst.

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show abstract

“…The Support Vector Machines allow one to build a classifier predicated upon training data by determining a linear separator in a specific feature dimension [15]. As seen in [16] we can create a knowledge base consisting of training and testing data using an appropriate power system model and simulator. Diversity of data points in the knowledge base can be achieved by incorporating load changes allowing multiple operating points [13], [16].…”

Section: Introductionmentioning

confidence: 99%

“…As seen in [16] we can create a knowledge base consisting of training and testing data using an appropriate power system model and simulator. Diversity of data points in the knowledge base can be achieved by incorporating load changes allowing multiple operating points [13], [16]. Simulators have been used prevalently to create data and work has been performed to show the agreement between different simulators [17].…”

Section: Introductionmentioning

confidence: 99%

A Learning Scheme for Microgrid Reconnection

Lassetter

Cotilla‐Sanchez

Kim

2018

IEEE Trans. Power Syst.

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This paper introduces a potential learning scheme that can dynamically predict the stability of the reconnection of sub-networks to a main grid. As the future electrical power systems tend towards smarter and greener technology, the deployment of self sufficient networks, or microgrids, becomes more likely. Microgrids may operate on their own or synchronized with the main grid, thus control methods need to take into account islanding and reconnecting of said networks. The ability to optimally and safely reconnect a portion of the grid is not well understood and, as of now, limited to raw synchronization between interconnection points. A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to stability or instability. A dynamics simulator fed with pre-acquired system parameters is used to create training data for the SVM in various operating states. The classifier was tested on a variety of cases and operating points to ensure diversity. Accuracies of approximately 85% were observed throughout most conditions when making dynamic predictions of a given network.

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“…Phasor measurement unit based fault location techniques are proposed in [28][29][30]. Other methods, such as an optimized extreme learning machine-based approach, use synchrophasors to ensure real-time power transient stability prediction [31].Up to now, a BILP model is written to its standard form in which the number of the constraints is equal to the number of optimization variables for the OPP problem [4][5][6][7][8][9][10]. This work introduces an underdetermined system of nonlinear equations, where the equations are fewer than the design variables for the solution of the OPP problem.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Towards Smart Energy Grids: A Box-Constrained Nonlinear Underdetermined Model for Power System Observability Using Recursive Quadratic Programming

Theodorakatos

Lytras

Babu³

2020

Energies

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This paper introduces an underdetermined nonlinear programming model where the equality constraints are fewer than the design variables defined on a compact set for the solution of the optimal Phasor Measurement Unit (PMU) placement. The minimization model is efficiently solved by a recursive quadratic programming (RQP) method. The focus of this work is on applying an RQP to attempt to find guaranteed global minima. The proposed minimization model is conducted on IEEE systems. For all simulation runs, the RQP converges superlinearly towards optimality in a finite number of iterations without to be rejected the full step-length. The simulation results indicate that the RQP finds out the minimal number and the optimal locations of PMUs to make the power system wholly observable.Energies 2020, 13, 1724 2 of 17 OPP problem is unraveled using the mixed-integer semi-definite programming approach subjected to linear matrix inequality. This technique is based on numerical observability, whereas most of the other techniques are based on topological observability, which may or may not ensure numerical observability to be executed successfully for state estimation. In [16,17], mixed-integer linear programming and nonlinear programming techniques are compared to check their suitability for networks of different sizes.Stochastic algorithms have also been used for the OPP problem. A recursive tabu search is suggested in [18], which has been claimed superior to multiple Tabu search and higher observability. Binary particle swarm optimization (BPSO) algorithm is developed for the solution of the OPP problem in [19]. In [20], the authors proposed a BPSO algorithm to minimize the number of substations in which installations must be performed for making all voltage levels observable while being subject to various practical constraints. Authors of [21] proposed the binary gravitational search algorithm to solve the OPP problem. Authors in [22], presented a modified binary Cuckoo optimization algorithm for the solution of the OPP problem with maximum redundancy. Genetic algorithms (GA) have been applied to determine the optimal allocation of PMUs in [23,24].Accurate knowledge of transmission system parameters, such as series impedance, optimizes distance relay settings and impedance-based fault location. A new method is developed to measure transmission line impedances and admittances from synchronized phasor measurements in [25].A new on-line method for estimating transmission line constants of a power system is proposed in [26]. In [27], a new State Estimation (SE) framework is proposed for processing Remote Terminal Unit (RTU) and PMU measurements separately in order to leave the traditional weighted least square (WLS)-based SE software unchanged. Phasor measurement unit based fault location techniques are proposed in [28][29][30]. Other methods, such as an optimized extreme learning machine-based approach, use synchrophasors to ensure real-time power transient stability prediction [31].Up to now, a BILP model is written to its st...

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Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

Cited by 55 publications

References 32 publications

Geographically Coordinated Primary Frequency Control

Geographically Coordinated Primary Frequency Control

A Learning Scheme for Microgrid Reconnection

Towards Smart Energy Grids: A Box-Constrained Nonlinear Underdetermined Model for Power System Observability Using Recursive Quadratic Programming

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