Neural Networks to Improve Distribution State Estimation—Volt Var Control Performances

Biserica, M.; Bésanger, Yvon; Caire, Raphaël; Chilard, Olivier; Deschamps, P.

doi:10.1109/tsg.2012.2193673

Cited by 45 publications

(17 citation statements)

References 24 publications

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“…K-matrix distribution power flow must be combined with optimization algorithm to estimate the Var value on PV bus. PSO was chosen in this research considering on literature [9]- [21] PSO is robust and simple optimization algorithm Particle Swarm Optimization (PSO) is heuristic algorithm that inspired by swarm of birds looking for food source [22]. PSO in K-matrix power flow is used for tuning Var in PV bus and keep the voltage constant.…”

Section: B K-matrik -Pso Power Flow For Active Distributionmentioning

confidence: 99%

Online State Estimator for Three Phase Active Distribution Networks Displayed on Geographic Information System

Fajar¹,

Suryawati²,

Penangsang³

et al. 2014

JOCET

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Abstract-To improve the operating performance of a distribution network, on line monitoring is required. For this purpose, sensors (metering devices) are installed. To reduce the number of sensors, state estimation approach can be used to estimate the voltage of buses which do not have sensors. This paper proposes online state estimator for three phase active distribution networks using Neural Network and displayed the results on Geographic Information System (GIS). Neural Network based state estimation is used to estimate the bus voltages by using learning approach from power flow patterns. K-matrix three phase distribution power flow is used in this method as an analytical tool. The K-matrix approach is combined with Particle Swarm Optimization (PSO) in handling a Distributed Generation (DG) which is operated as a voltage controlled (PV) bus. The test results show that the proposed method can reduce the number of sensors significantly (almost 50%). Index Terms-rk, K-matrrix, PSO and GIS.

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Section: B K-matrik -Pso Power Flow For Active Distributionmentioning

confidence: 99%

Online State Estimator for Three Phase Active Distribution Networks Displayed on Geographic Information System

Fajar¹,

Suryawati²,

Penangsang³

et al. 2014

JOCET

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“…Most of the studies have focused on short-term/mid-term load forecasting of loads. Few works have employed Neural Networks in distribution network optimization [19]- [20]. Hence, employing reallsemi-real time data of Smart Meters for designing a predictive VVO could be regarded as a great novelty.…”

Section: Introductionmentioning

confidence: 99%

Predictive algorithm for Volt/VAR optimization of distribution networks using Neural Networks

Manbachi

Farhangi

Palizban

et al. 2014

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

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Smart Grid functions such as Advanced Metering Infrastructure, Pervasive Control and Distribution Management Systems have brought numerous control and optimization opportunities for distribution networks through more accurate and reliable techniques. This paper presents a new predictive approach for Volt/VAr Optimization (VVO) of smart distribution systems using Neural Networks (NN) and Genetic Algorithm (GA). The proposed predictive algorithm is capable of predicting the load profile of target nodes a day ahead by employing the historical metrology data of Smart Meters, It can further perform a comprehensive VVO in order to minimize distribution network loss/operating costs and run Conservation Voltage Reduction (CVR) to conserve more energy. To test the merits of the proposed algorithm, British Columbia Institute of Technology north campus distribution grid is used as research case study.

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“…Soft Computing Techniques (Artificial Neural Networks [5], Genetic Algorithms, Fuzzy Logic Models and Particle Swarm Techniques) provide better performance than conventional methods. It is reported in those that evolutionary or heuristic algorithms are more efficient than classical algorithms for solving the ORPD problem.…”

Section: Iintroductionmentioning

confidence: 99%

Reactive power optimization using GSA

Ramamoorthy¹,

Ramachandran²

2014

2014 6th IEEE Power India International Conference (PIICON)

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Reactive power flow in a power system is to be optimized by minimizing the real power loss and it is essential for maintaining the voltage stability of the system. The control variables such as magnitude of generator bus voltage, transformer tap settings and shunt capacitors can optimize the reactive power flow. This paper uses the nature inspired gravitational search algorithm (GSA) to solve a multi objective and multi constrained optimal reactive power flow problem in power systems. The performance of the standard IEEE 30 bus test system is tested and the results prove the effectiveness of this algorithm. The results show best among other algorithms and found to be suitable for power system operation optimization problems.

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Neural Networks to Improve Distribution State Estimation—Volt Var Control Performances

Cited by 45 publications

References 24 publications

Online State Estimator for Three Phase Active Distribution Networks Displayed on Geographic Information System

Online State Estimator for Three Phase Active Distribution Networks Displayed on Geographic Information System

Predictive algorithm for Volt/VAR optimization of distribution networks using Neural Networks

Reactive power optimization using GSA

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