Applications of Novel Hybrid Bat Algorithm With Constrained Pareto Fuzzy Dominant Rule on Multi-Objective Optimal Power Flow Problems

Chen, Gonggui; Qian, Jie; Zhang, Zhizhong; Zhi, Sun

doi:10.1109/access.2019.2912643

Cited by 39 publications

(24 citation statements)

References 43 publications

(70 reference statements)

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“…On the other hand, mono-objective OPF is becoming unable to meet the diverse needs of optimal operation of power systems. In this context, multi-objective OPF (MOPF) has attracted growing concerns [15][16][17], since it can coordinate multiple and possibly conflicting objectives. Therefore, this work focuses on the corrective securityconstrained MOPF (SC-MOPF) for the AC/VSC-MTDC system.…”

Section: A Background and Motivationmentioning

confidence: 99%

Security-Constrained Multi-Objective Optimal Power Flow for a Hybrid AC/VSC-MTDC System With Lasso-Based Contingency Filtering

2020

IEEE Access

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In order to coordinate the economy and voltage quality of a meshed AC/VSC-MTDC system, a new corrective security-constrained multi-objective optimal power flow (SC-MOPF) method is presented in this paper. A parallel SC-MOPF model with N-1 security constraints is proposed for corrective control actions of the meshed AC/DC system, in which the minimization of the generation cost and voltage deviation are used as objective functions. To solve this model, a novel parallel bi-criterion evolution indicator based evolutionary algorithm (BCE-IBEA) algorithm is developed to seek multiple well-spread Pareto-optimal solutions through the introduction of parallel computation. In this process, a least absolute shrinkage and selection operator (Lasso)-based N-1 contingency filtering scheme with a composite security index is developed to efficiently screen out the most severe cases from all contingencies. And thereby, the best compromise solutions reflecting the preferences of different decision makers are automatically determined via an integrated decision making technique. Case studies in the modified IEEE 14-and 300bus systems demonstrate that the presented approach manages to address this SC-MOPF problem with significantly improved computational efficiency.INDEX TERMS AC/DC system, multi-objective optimization, optimal power flow, VSC-HVDC, decision making, contingency filtering ,2 c PI are the c PI values, which are respectively obtained by using the Lasso approach and the direct computation according to (19).

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Section: A Background and Motivationmentioning

confidence: 99%

Security-Constrained Multi-Objective Optimal Power Flow for a Hybrid AC/VSC-MTDC System With Lasso-Based Contingency Filtering

2020

IEEE Access

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“…A novel approach to improved bat algorithm (BA) performance was proposed and applied on multi-objective OPF problem [35]. In this research, the authors improved local search ability of BA by using monotone random filling model based on extreme learning (MRFME), and they enhanced global search by mutation and crossover.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

2019

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Harris' hawk optimization (HHO) is a recent addition to population-based metaheuristic paradigm, inspired from hunting behavior of Harris' hawks. It has demonstrated promising search behavior while employed on various optimization problems, however the diversity of search agents can be further enhanced. This paper represents a novel modified variant with a long-term memory concept, hence called long-term memory HHO (LMHHO), which provides information about multiple promising regions in problem landscape, for improvised search results. With this information, LMHHO maintains exploration up to a certain level even until search termination, thus produces better results than the original method. Moreover, the study proves that appropriate tools for in-depth performance analysis can help improve search efficiency of existing metaheuristic algorithms by making simple yet effective modification in search strategy. The diversity measurement and exploration-exploitation investigations prove that the proposed LMHHO maintains trade-off balance between exploration and exploitation. The proposed approach is investigated on high-dimensional numerical optimization problems, including classic benchmark and CEC'17 functions; also, on optimal power flow problem in power generation system. The experimental study suggests that LMHHO not only outperforms the original HHO but also various other established and recently introduced metaheuristic algorithms. Although, the research can be extended by implementing more efficient memory archive and retrieval approaches for enhanced results. INDEX TERMS Diversity measurement, exploration-exploitation, long-term memory, Harris' hawk optimization, optimal power flow.

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“…where N i , N and N PQ are the numbers of the nodes linked to the ith node, the nodes except the slack one and the PQ nodes. The definitions of other mentioned parameters are clarified in literatures [7], [12], [18].…”

Section: ) Equality Restrictionsmentioning

confidence: 99%

“…The transformer taps of the IEEE 30-bus system, which includes 6 generators and 24-dimensional control variables, 3. The structure and more details of IEEE 30-bus system can be found in literatures [7], [12], [31], [32].…”

Section: A Systems and Objective Combinationsmentioning

confidence: 99%

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Multi-Objective Optimal Power Flow Based on Hybrid Firefly-Bat Algorithm and Constraints- Prior Object-Fuzzy Sorting Strategy

et al. 2019

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In this paper, a novel hybrid firefly-bat algorithm with constraints-prior object-fuzzy sorting strategy (HFBA-COFS) is put forward to solve the strictly-constrained multi-objective optimal power flow (MOOPF) problems. The hybrid firefly-bat algorithm (HFBA) integrates the dimension-based firefly algorithm and the modified bat algorithm to improve the population-diversity and global-exploration ability of original algorithm. To handle the unqualified state variables and overcome the deficiency of traditional penalty function approach (PFA), the constraints-prior Pareto-dominant rule (CPR) which takes constraintsviolation and objectives-value into consideration is proposed in this paper. Furthermore, an effective constraints-prior object-fuzzy sorting (COFS) strategy based on CPR rule is presented to seek the welldistributed Pareto optimal set (POS) in solving the MOOPF problems. To validate the great advantages of HFBA-COFS algorithm, ten MOOPF cases optimizing active power loss, total emission and fuel cost are simulated on the IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems. In addition, the generational distance and SPREAD evaluation indexes powerfully demonstrate that the proposed HFBA-COFS algorithm can achieve high-quality POS, which has great significance to realize the safe and economic operation of large-scale power systems.INDEX TERMS Hybrid firefly-bat algorithm, constraints-prior object-fuzzy sorting strategy, multi-objective optimal power flow problem, economic operation.

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Applications of Novel Hybrid Bat Algorithm With Constrained Pareto Fuzzy Dominant Rule on Multi-Objective Optimal Power Flow Problems

Cited by 39 publications

References 43 publications

Security-Constrained Multi-Objective Optimal Power Flow for a Hybrid AC/VSC-MTDC System With Lasso-Based Contingency Filtering

Security-Constrained Multi-Objective Optimal Power Flow for a Hybrid AC/VSC-MTDC System With Lasso-Based Contingency Filtering

Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

Multi-Objective Optimal Power Flow Based on Hybrid Firefly-Bat Algorithm and Constraints- Prior Object-Fuzzy Sorting Strategy

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