Design and real-time implementation of optimal power system wide area system-centric controller based on temporal difference learning

Yousefian, Reza; Kamalasadan, Sukumar

doi:10.1109/ias.2014.6978407

Cited by 11 publications

(12 citation statements)

References 12 publications

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“…Oscillatory angle instability relates to the problem of low-frequency oscillations in the system (local modes in the range [0.7 − 2.0] Hz and inter-area modes in the range [0.1 − 0.8] Hz). This type of instability was considered in the context of controlling individual system components [17,[58][59][60][65][66][67][68][69] and wide-area controls [70][71][72]. Some of the works [17,[58][59][60] are already discussed in the context of transient angle instability and some comments are valid for oscillatory angle instability consideration.…”

Section: Emergency Controlmentioning

confidence: 99%

(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

Glavić

2019

Annual Reviews in Control

111

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This paper reviews existing works on (deep) reinforcement learning considerations in electric power system control. The works are reviewed as they relate to electric power system operating states (normal, preventive, emergency, restorative) and control levels (local, household, microgrid, subsystem, wide-area). Due attention is paid to the control-related problems considerations (cyber-security, big data analysis, short-term load forecast, and composite load modelling). Observations from reviewed literature are drawn and perspectives discussed. In order to make the text compact and as easy as possible to read, the focus is only on the works published (or "in press") in journals and books while conference publications are not included. Exceptions are several work available in open repositories likely to become journal publications in near future. Hopefully this paper could serve as a good source of information for all those interested in solving similar problems.

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Section: Emergency Controlmentioning

confidence: 99%

(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

Glavić

2019

Annual Reviews in Control

111

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“…where, U is the utility function and γ is the discount factor [8]. An explicit utility function is used for reward/punishment for RL, or as incremental cost function in Lyapunov stability concept based on,…”

Section: Supervised Reinforcement Learning Updatementioning

confidence: 99%

“…In the online process, these controllers revive a learned policy in the presence of an associated input or generalizes for a new input. Neural Network (NN) as a distinct learning-based function approximator has been effectively implemented as a power system intelligent controller in several works [7][8][9], and their ability to adapt during nonlinear transient conditions have been discussed [7][8][9][10][11][12].These architectures use NNs in the form of supervised learning as an intelligent PSS for damping generator oscillations. However, majority of these works have used the intelligent controller by itself.…”

Section: Introductionmentioning

confidence: 99%

“…The main advantages of the RL over supervised learning technique is its ability to: be used in learning problems involving sequential dynamics, optimize a scalar performance objective, and reduce the computational burden of offline training process. However, the heuristic nature of this type of controllers still causes stability issues; thus cannot be fully trusted in real-life implementation even tough being tested in real-time simulations [7,8]. Therefore, the need to develop a more advanced hybrid design that provides stable control actions under unforeseen disturbances is very clear and that is the main focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

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A Lyapunov function based optimal hybrid power system controller for improved transient stability

Yousefian

Kamalasadan

2016

Electric Power Systems Research

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In this paper, an intelligent power system stabilizer based on a stable and optimal hybrid learning-based adaptive control architecture is proposed which is evolved from approximate dynamic programming technique. The hybrid learning controller is based on two algorithms a) A reinforcement learning neural network controller implemented by Adaptive Critic Design (ACD) and b) A model reference adaptive controller (MRAC). The proposed method uses a novel value priority architecture for integrating these two algorithms. The designed ACD approximates nonlinear functional dynamics of the power system and interacts with the MRAC that adapts based on parametric changes. The value priority scheme developed based on a softmax network generates a hybrid control signal derived from a Lyapunov stability function that evaluates identification, performance, and stability of each controller. The overall hybrid control algorithm ensures convergence to an optimal control solution without using an explicit model of the system and at the same time ensures overall system stability. Theoretical results are validated by simulation studies for electric-generator stabilization on a 2-area 5-generator power system and as a wide-area controller on IEEE 68-bus system.

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“…This information layer is formed from two major classes of data: the first class contains the records collected from the status of different parameters in the network such as bus voltages, powers, currents, and so on [2], while the second class comprises the controlling commands which are fed back to the network from decision making units [13]. The supervisory control and data acquisition (SCADA) system in addition to the technology of the wide area monitoring system (WAMS) can provide such massive voltage and power data in near real time [2]- [3].…”

Section: Introductionmentioning

confidence: 99%

Smart-Grid Topology Identification Using Sparse Recovery

Babakmehr

Simões

Wakin

et al. 2016

IEEE Trans. on Ind. Applicat.

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Smart grid (SG) technology reshapes the traditional power grid into a dynamical network with a layer of information that flows along the energy system. Recorded data from a variety of parameters in SGs can improve analysis of different supervisory problems, but an important issue is their cost and power efficiency in data analysis procedures. This paper develops an efficient solution for power network topology identification (PNTI) and monitoring activities in SG. The basic idea combines optimization-based sparse recovery techniques with a graph theory foundation. The power network (PN) is modeled as a large interconnected graph, which can be evaluated with the DC power-flow model. It has been shown that topology identification for such a system can mathematically be reformulated as a sparse recovery problem (SRP), and the corresponding SRP can efficiently be solved using SRP solvers. In this work we especially exploit the concentration of nonzero elements in the corresponding sparse vectors around the main diagonal in the nodal-admittance or structure matrix of the PN to improve the results. The network models have been generated with the MATPOWER toolbox, and Matlab-based simulation results have indicated the promising performance of the proposed method for real time TI in SGs.

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Design and real-time implementation of optimal power system wide area system-centric controller based on temporal difference learning

Cited by 11 publications

References 12 publications

(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

A Lyapunov function based optimal hybrid power system controller for improved transient stability

Smart-Grid Topology Identification Using Sparse Recovery

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