Hopfield network and parallel genetic algorithm for solving state estimate in power systems

Khoa, T.Q.D.; Binh, Phan Thi Thanh; Khoa, T.V.

doi:10.1109/icpst.2004.1460111

Cited by 7 publications

(7 citation statements)

References 10 publications

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“…Early approaches, such as [7] from García-Lagos et al, consider ANN as the solver for the WLS SE optimization problem. In [8], Khoa et al present two different methods for SE: Both a neural network and a parallel genetic algorithm estimate the state vector by minimizing the WLS fitness function. In [9], Singh et al perform DSSE with ANN on the IEEE 14 system and 2000 load cases which differ up to ±15 % in power.…”

Section: Introductionmentioning

confidence: 99%

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

Menke

Bornhorst

Braun

2019

International Journal of Electrical Power & Energy Systems

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The increasing number of distributed generators connected to distribution grids requires a reliable monitoring of distribution grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First approaches using a limited number of measurements to monitor distribution grids exist, some of which use artificial neural networks (ANN). The current ANN-based approaches, however, are limited to static topologies, only estimate voltage magnitudes, do not work properly when confronted with a high amount of distributed generation and often yield inaccurate results. These strong limitations have prevented a true applicability of ANN for distribution system monitoring. The objective of this paper is to overcome the limitations of existing approaches. We do that by presenting an ANN-based scheme, which advances the state-of-the-art in several ways: Our scheme can cope with a very low number of measurements, far less than is traditionally required by the state-of-the-art weighted least squares state estimation (WLS SE). It can estimate both voltage magnitudes and line loadings with high precision and includes different switching states as inputs. Our contribution consists of a method to generate useful training data by using a scenario generator and a number of hyperparameters that define the ANN architecture. Both can be used for different power grids even with a high amount of distributed generation. Simulations are performed with an elaborate evaluation approach on a real distribution grid and a CIGRE benchmark grid both with a high amount of distributed generation from photovoltaics and wind energy converters. They demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors when comparing voltage magnitude and line magnitude estimation errors.

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Section: Introductionmentioning

confidence: 99%

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

Menke

Bornhorst

Braun

2019

International Journal of Electrical Power & Energy Systems

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“…In Khoa et al, the authors presented the artificial neural network for static state estimation [19]. A Hopfield neural network (HNN) and parallel genetic algorithms (PGA) are employed to solve static state estimation on the 5-bus test system.…”

Section: Introductionmentioning

confidence: 99%

Power system state estimation solution using modified models of PSO algorithm: Comparative study

2016

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“…HNNs can be classified into two forms: discrete-time and continuous-time models that generalize the discrete case. The energy function of the continuous Hopfield network (CHN) is given in terms of the weight matrix and the bias vector [14,16,31]. Then, the CHN system evolves in the direction of a decrease in the value of the energy function.…”

Section: Introductionmentioning

confidence: 99%

On Hyperparameters Optimization of the Continuous Hopfield Network via Genetic Algorithm

Safae,

Haddouch,

MOUTAOUAKIL

2023

Preprint

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Recurrent neural networks have proven to be effective in various domains due to their ability to remember key information during solution processes. The continuous Hopfield network is a recurrent neural network that can solve several complicated problems, including combinatorial optimization problems. The user of a continuous Hopfield network for solving combinatorial optimization problems must construct an energy function that combines the energy function and the constraints; this combination requires penalty hyper-parameters that have a direct impact on the quality of the solution and its feasibility. To ensure the convergence of the continuous Hopfield network and thus ensure the feasibility of the solutions to the combinatorial problems, we introduce a linear optimization model given by: the objective function represents the energy function of continuous Hopfield network, which controls the quality of the solution, and the constraints represent conditions on the parameters of the continuous Hopfield network penalty function extracted by the hyperplane procedure, which ensure the feasibility of the solutions. On the well-known NP-complete problem (task assignment problem, traveling salesman problem, weighted constraint satisfaction problem, max-stable problem, graph coloring problem, shortest path problem and portfolio selection problem) the genetic algorithm is used to solve the proposed model. The proposed method has shown its superiority over random methods for choosing continuous Hopfield network hyper-parameters: the solutions produced are all feasible, and the difference in accuracy between our method and random methods is 48.8%.

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Hopfield network and parallel genetic algorithm for solving state estimate in power systems

Cited by 7 publications

References 10 publications

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

Power system state estimation solution using modified models of PSO algorithm: Comparative study

On Hyperparameters Optimization of the Continuous Hopfield Network via Genetic Algorithm

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