Artificial intelligence assisted voltage stability enhancement

Madan, Sanjeev; Bollinger, K.E.

doi:10.1109/ccece.1996.548119

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Since the 1990s, electric power companies have gradually adopted AI technologies to improve the decision‐making capability of power grid dispatching . Among these, Electricite de France (EDF) studies the application of rule‐based decision tree and knowledge‐based expert system in power grid stability assessment and decision making . The Empros Power Control Center in the United States attempts to apply intelligent tools to the energy management system (EMS) at the time .…”

Section: The Conception Of Parallel Dispatchmentioning

confidence: 99%

“…65 Among these, Electricite de France (EDF) studies the application of rule-based decision tree and knowledge-based expert system in power grid stability assessment and decision making. 66 The Empros Power Control Center in the United States attempts to apply intelligent tools to the energy management system (EMS) at the time. 67 The Scottish Power Company adopts the iKue solution to sort out the experiences of the dispatchers and engineers as knowledge, achieving transfer of knowledge within the organization.…”

Section: Evolution Of Smart Dispatching In Eeps Fieldmentioning

confidence: 99%

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Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Cheng

2019

Int J Energy Res

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Summary Energy internet (EI) is a complex coupled multienergy system; it is essential to investigate its multienergy dispatching optimization issues. To this end, this paper first proposes a novel conception of smart dispatching for EI with a complex cyber‐physical‐social system (CPSS) network from the perspective of parallel dispatch, called parallel dispatching robot (PDR), and investigates the implementations of PDR based on smart artificial society (SAS) modeling. First, we introduce EI and describe the dispatching issues of EI. Second, we discuss several important concepts supporting the parallel dispatch conception of EI, including knowledge automation (KA), CPSS, and parallel machine learning (PML). On the basis of these, we elaborate the concept of parallel dispatch. Moreover, we construct a large closed‐loop feedback control framework of parallel dispatch for EI integrating a CPSS network based on KA and PML. Third, we establish an experimental platform for PDR research based on the proposed parallel dispatch framework. Fourth, we develop the PML‐based SAS models of a single PDR in centralized dispatching modes and group PDRs in decentralized dispatching modes to achieve crowd wisdom emergence and performance improvement in current cyber‐physical system frameworks of EI. Moreover, we design an external global closed loop for PDR to evaluate its operation stability. Lastly, we conduct a detailed discussion on PDR and offer some prospects for its engineering implementations. The biggest innovation of this paper lies in systematically proposing the smart dispatching concept and framework for complex CPSS‐based EI from the perspective of parallel dispatch and thoroughly investigating how to use SAS modeling to implement parallel dispatching and control for EI considering human and social factors, which is a major extension and theoretical improvement to existing single smart wide area robot concept and a preliminary attempt in investigating a shift from Energy 4.0 to Energy 5.0 in China.

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Section: The Conception Of Parallel Dispatchmentioning

confidence: 99%

Section: Evolution Of Smart Dispatching In Eeps Fieldmentioning

confidence: 99%

Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Cheng

2019

Int J Energy Res

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Development of an integrated intelligent system

Madan

Son²,

Bollinger³

1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM. 10 Years Networking the Pacific Ri

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This paper describes t h e ongoing development of an integrated intelligent system for voltage and reactive power management in power systems. The developed system incorporates an expert system that guides the operator in making proper decisions for voltage/VAr violation and voltage stability problems. Once a decision regarding the new controller settings has been made, t h e embedded neural network predicts t h e new power flows over the transmission lines. I N T R O D U C T I O NThe voltage and VAr issues have become a significant problem in recent years. Due to economic and environmental reasons the power system equipment is operating at close to its limits. The interconnections in the power system adds to the complexity of maintaining and running the power system optimally. One outcome of the above factors is the voltage stability and voltage/VAr violation problems.Voltage stability is the ability of the power system to maintain steady state acceptable voltages at all buses in the system under normal conditions and after being subjected to disturbances. A system is in a state of voltage instability when a disturbance, increase in load demand or a change in system condition causes a progressive and uncontrollable drop in voltage. The main factor causing voltage instability is the reactive power mismatch in the power system. Voltage collapse is the process by which voltage instability leads to a very low voltage profile in a significant part of the system [l]. Voltage/VAr violation problem is characterized by violations of limits of reactive power of generators and/or voltages of load buses. If voltage/VAr violations are left unattended or not dealt with properly, there is a danger of voltage/VAr violations developing into voltage stability problem. Emergency situations such as voltage stability and voltage/VAr violations call for quick diagnosis and deci-O-7803-39O5-3/97/$1O.O00 1997 IEEE sion making by the operator. For these kind of complex situations the operator can quickly reaches a cognitive barrier. Artificial intelligence [2] techniques, can provide valuable support tool to operators under these conditions. Artificial Intelligence techniques, mainly in the form of expert systems, have been developed successfully for various facets of power system operation [3].This paper describes the development of a Comprehensive Voltage and VAr Controller (CVVC), an integrated intelligent system for voltage/VAr related problems which will assist operators in making two types of decisions:1. Suggest new setting of controllers for voltage/VAr violation problem (with the system being voltage stable). 2.Suggest where and how much load to shed, for voltage stability problem and when there are insufficient resources available to correct the voltage/VAr violation problem.These decisions will be made by a combination of an expert system and analytical techniques. Once a new setting of controllers have been arrived at, the embedded neural network [4] predicts the new power flows over the transmission lines. The CVVC has the ...

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Artificial intelligence assisted voltage stability enhancement

Cited by 2 publications

References 13 publications

Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Development of an integrated intelligent system

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