A Novel PSOS-CGSA Method for State Estimation in Unbalanced DG-Integrated Distribution Systems

Ullah, Zia; Elkadeem, M.R.; Wang, Shaorong; Radosavljević, Jordan

doi:10.1109/access.2020.3003521

Cited by 19 publications

(14 citation statements)

References 32 publications

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“…As more information is used in the estimation framework, the uncertainty in the estimation can be decreased. To further increase the estimation performance, future research directions include: systematically study the features related to the dynamics of sulfur concentrate grade; embed more information related to the dynamics of sulfur concentrate grade in the estimation framework, e.g., trend information [36]; increase the optimization performance of the estimation algorithm [37]; combine deep learning, reinforcement learning and control theory with the estimation framework [38][39][40][41]; design robust moving horizon estimation to increase the stability of estimation performance.…”

Section: Discussionmentioning

confidence: 99%

Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions

Sun

Zhou

et al. 2020

IEEE Access

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This paper proposed a moving horizon approach to predict sulfur concentrate grade in a sulfur flotation process. In this approach, the grade prediction problem is formulated as a moving horizon estimation problem. To build the nominal model for the estimation, the kinetic of sulfur flotation process are firstly studied. The unknown variable in the kinetic model, i.e., the flux of overflow, is obtained using machine vision technology. Moreover, to account for the multi-modal characteristic of sulfur flotation process, the kinetic model parameters are identified for different working conditions. To eliminate the effect of , a robust parameter identification approach is adopted. Finally, the kinetic model is embedded in the moving horizon estimation framework, which reconstructs the concentrate grade using from online measured process variables. Experimental results demonstrate the feasibility and efficient of the proposed method.INDEX TERMS moving horizon estimation, kinetic modeling, parameter identification, mineral processing

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Section: Discussionmentioning

confidence: 99%

Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions

Sun

Zhou

et al. 2020

IEEE Access

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“…PSO has been widely applied to optimize the performance of electrical power systems including economic dispatch [237][238][239], optimal power flow [240][241][242], state estimation [243], power system controllers [244,245], unit commitment [246] and capacitor placement [247]. A detailed and thorough survey on the applications of PSO in electrical power systems is provided in [248].…”

Section: Electrical Power Systemsmentioning

confidence: 99%

“…The proposed technique can achieve lower power loss when compared with the individual PSO and ABC algorithms; nevertheless, its performance is not compared with high-performance PSO variants as well as with other well-known meta-heuristic algorithms such as GWO and whale optimization algorithm (WOA). The work in [243] hybridized PSO with gravitational search algorithm to solve the problem of state estimation in distribution systems. Results have shown that the proposed hybrid scheme is more accurate and reliable than the standard PSO algorithm and the original GSA approach.…”

Section: Electrical Power Systemsmentioning

confidence: 99%

“…Adaptive PSO [269], exponential decay PSO (EDPSO) [270], PSO with chaos theory [271], hybrid PSO and gravitational search algorithm (PSO-CGSA) [243] Power System controllers Hybrid PSO-pattern search [244], PSO with Pade approximation [245] Capacitor Placement Constriction factor PSO [272], hybrid PSO [247] Generation expansion problem Comprehensive learning PSO [273], fuzzy adaptive chaotic binary PSO [274] Power system reliability and security…”

Section: State Estimationmentioning

confidence: 99%

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Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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“…The equality constraints for the ORPD problem are typical nonlinear power flow equations for three-phase unbalanced DNs [22,23].…”

Section: Equality Constraintsmentioning

confidence: 99%

Dynamic Optimal Power Dispatch in Unbalanced Distribution Networks with Single-Phase Solar PV Units and BESS

Radosavljević¹,

Ktena²,

Gajic³

et al. 2023

Preprint

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The battery energy storage systems (BESS) are most promising solution for increasing efficiency and flexibility of distribution networks (DNs) with significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV sys-tems and BESS that need to be addressed to maximize DN performance. This paper deals with the day-ahead optimal active-reactive power dispatching in unbalanced DNs with integrated sin-gle-phase PV generation and BESSs. The objectives are the minimization of cost for electricity, en-ergy losses in the DN and voltage unbalance at three-phase load buses by optimal managing of active and reactive power flows. To solve this highly constrained nonlinear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and cha-otic gravitational search algorithm (CGSA), called PSOS-CGSA algorithm, is proposed. A scenario based approach encompassing the Monte Carlo simulation (MCS) method with the simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through the series test cases in a modified IEEE 13-bus feeder.

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A Novel PSOS-CGSA Method for State Estimation in Unbalanced DG-Integrated Distribution Systems

Cited by 19 publications

References 32 publications

Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions

Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions

Particle Swarm Optimization: A Comprehensive Survey

Dynamic Optimal Power Dispatch in Unbalanced Distribution Networks with Single-Phase Solar PV Units and BESS

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