Learning-coordinated fuzzy logic control of dynamic quadrature boosters in multi-machine power systems

Li, B.H.; Wu, Pan; Wang, P.Y.; Zhou, Xiaoxin

doi:10.1049/ip-gtd:19990426

Cited by 11 publications

(12 citation statements)

References 15 publications

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“…The earliest research reports date back to 1999 and 2000 [19,22,23]. In [19] the use of RL in electric power system closed-loop emergency control, were investigated.…”

Section: Results Under Cyclic Power Demand Variationsmentioning

confidence: 99%

“…A non-model based RL technique was employed to search for optimal fuzzy-logic controller parameters according to a given performance index, to control the boosters in a coordinated fashion. Using a non-model based RL algorithm to optimize synchronous generator PID controller parameters was explored in [22]. The works presented in [22,23] are good examples on how the RL methods can be combined with existing local controllers where the learning component learns only a setting of the parameters while the local controller assures a baseline control performance.…”

Section: Results Under Cyclic Power Demand Variationsmentioning

confidence: 99%

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Power Systems Stability Control: Reinforcement Learning Framework

Ernst

Glavić

Wehenkel

2004

IEEE Trans. Power Syst.

183

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Abstract-In this paper we explore how a computational approach to learning from interactions, called Reinforcement Learning (RL), can be applied to control power systems. We describe some challenges in power system control and discuss how some of those challenges could be met by using these RL methods. The difficulties associated with their application to control power systems are described and discussed as well as strategies that can be adopted to overcome them. Two reinforcement learning modes are considered : the on-line mode in which the interaction occurs with the real power system and the off-line mode in which the interaction occurs with a simulation model of the real power system. We present two case studies made on a 4-machine power system model. The first one concerns the design by means of RL algorithms used in off-line mode of a dynamic brake controller. The second concerns RL methods used in online mode when applied to control a Thyristor Controlled Series Capacitor (TCSC) aimed to damp power system oscillations.

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“…The earliest research reports date back to 1999 and 2000 [19,22,23]. In [19] the use of RL in electric power system closed-loop emergency control, were investigated.…”

Section: Results Under Cyclic Power Demand Variationsmentioning

confidence: 99%

Section: Results Under Cyclic Power Demand Variationsmentioning

confidence: 99%

Power Systems Stability Control: Reinforcement Learning Framework

Ernst

Glavić

Wehenkel

2004

IEEE Trans. Power Syst.

183

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“…The work presented in Glavic et al (2005a) suggested fusion of RL and the concept of control Lyapunov functions (this approach is further elaborated in Glavic et al (2006)). Other works suggesting fusion of RL with known control techniques include Ernst et al (2009);Wang et al (2014) where RL was considered together with model predictive control, and Li and Wu (1999) where RL is combined with fuzzy logic control.…”

Section: Past and Recent Considerations Of Rl For Electric Power Systmentioning

confidence: 99%

“…Power system components considered include: dynamic brake Ernst et al (2004); Glavic (2005), thyristor controlled series capacitor Ernst et al (2004Ernst et al ( , 2009, quadrature booster Li and Wu (1999), synchronous generators (all AGC related references), individual or aggregated loads Vandael et al (2015); Ruelens et al (2016), etc. If used as a multi-agent system, then additional state variables must be introduced to ensure convergence of these essentially distributed computation schemes, and an adapted variant of standard RL methods is often used (for example correlated equilibrium Q(λ) Yu et al (2012a)).…”

Section: Past and Recent Considerations Of Rl For Electric Power Systmentioning

confidence: 99%

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

2017

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Abstract:In this paper, we review past (including very recent) research considerations in using reinforcement learning (RL) to solve electric power system decision and control problems. The RL considerations are reviewed in terms of specific electric power system problems, type of control and RL method used. We also provide observations about past considerations based on a comprehensive review of available publications. The review reveals the RL is considered as viable solutions to many decision and control problems across different time scales and electric power system states. Furthermore, we analyse the perspectives of RL approaches in light of the emergence of new-generation, communications, and instrumentation technologies currently in use, or available for future use, in power systems. The perspectives are also analysed in terms of recent breakthroughs in RL algorithms (Safe RL, Deep RL and path integral control for RL) and other, not previously considered, problems for RL considerations (most notably restorative, emergency controls together with so-called system integrity protection schemes, fusion with existing robust controls, and combining preventive and emergency control).

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“…The work from Liu et al (2000) is further extended in Jung et al (2002) as a feasibility study of a RL method for an agent's adaptive learning capability with load shedding control schemes. An investigation of a learning coordinated fuzzy-logic control strategy, based on the interconnected learning automata, for the control of dynamic quadrature boosters, installed distributively in a power system, to enhance power system stability is reported in Li and Wu (1999). A non model-based RL technique was employed to search for optimal fuzzy-logic controller parameters (the optimal fuzzy membership functions) according to a given performance index, to control the boosters in a coordinated fashion.…”

Section: Related Workmentioning

confidence: 99%

Approximate Value Iteration in the Reinforcement Learning Context. Application to Electrical Power System Control.

Ernst¹,

Glavić²,

Geurts³

et al. 2005

International Journal of Emerging Electric Power Systems

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In this paper we explain how to design intelligent agents able to process the information acquired from interaction with a system to learn a good control policy and show how the methodology can be applied to control some devices aimed to damp electrical power oscillations. The control problem is formalized as a discrete-time optimal control problem and the information acquired from interaction with the system is a set of samples, where each sample is composed of four elements: a state, the action taken while being in this state, the instantaneous reward observed and the successor state of the system. To process this information we consider reinforcement learning algorithms that determine an approximation of the so-called Q-function by mimicking the behavior of the value iteration algorithm. Simulations are first carried on a benchmark power system modeled with two state variables. Then we present a more complex case study on a four-machine power system where the reinforcement learning algorithm controls a Thyristor Controlled Series Capacitor (TCSC) aimed to damp power system oscillations.

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Learning-coordinated fuzzy logic control of dynamic quadrature boosters in multi-machine power systems

Cited by 11 publications

References 15 publications

Power Systems Stability Control: Reinforcement Learning Framework

Power Systems Stability Control: Reinforcement Learning Framework

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

Approximate Value Iteration in the Reinforcement Learning Context. Application to Electrical Power System Control.

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