Search

This paper presents a novel approach for reactive power planning of a connected power network. Reactive power planning is nothing but the optimal usage of all reactive power sources i.e., transformer tap setting arrangements, reactive generations of generators and shunt VAR compensators installed at weak nodes. Shunt VAR compensator placement positions are determined by a FVSI (Fast Voltage Stability Index) method. Optimal setting of all reactive power reserves are determined by a GA (genetic algorithm) based optimization method. The effectiveness of the detection of the weak nodes by the FVSI method is validated by comparing the result with two other wellknown methods of weak node detection like Modal analysis and the L-index method. Finally, FVSI based allocation of VAR sources emerges as the most suitable method for reactive power planning.

This research work proposes a planning strategy pertaining to the techno-economic operation of Indian power systems. The proposed strategy focused on the reactive power (VAr) planning (RPP) subject to the system operating cost minimization ensuring system security. To mitigate the RPP issue, unique meta-heuristic hybridized techniques are adopted to size the optimal parameter settings such as alternator's reactive power, tap settings of transformers etc. instigated by power flow analysis satisfying all equality and inequality constraints. The controlling variables are optimally determined to solve this non-linear RPP problem. The bottleneck for the installation of static VAr compensators at weak buses is also wiped out by different analytical techniques viz. loss sensitivity, power flow and modal analysis. Two different inter-regional transmission networks prevailing in India are considered to measure the adaptability, efficacy and efficiency of the proposed approach. The results obtained by applying the proposed approach have confirmed that network loss-minimization procedures produce an acceptable solution and reduce the operating cost which is especially important in RPP. The responses of bench mark functions and statistical analysis of the outcomes from both systems allow us to assess the overall efficiency and efficacy of the proposed techno-economic planning approach. [ABSTRACT FROM AUTHOR]

In a competitive electricity power market, reactive power planning (RPP) is a pivotal matter for the power system researchers from operational and economical view points. RPP is concerned with the installation or removal of reactive power equipment in power system. This article proposes an effective way to find out the optimal parameter settings related to the system active power loss as well as operational cost. Genetic Algorithm (GA)-based strategy is applied to determine the optimal values of reactive power generation of generators, size of shunt capacitors, transformer tap settings prior to minimizing the system operating cost due to active power loss, installation cost of shunt capacitors at the weak nodes, and the cost of line-charging elements. The proposed approach is applied to IEEE 30 and IEEE 57 bus test system. Finally, a comparative analysis has been done to validate the efficacy of GA-based approach with some well-established meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a new meta‐heuristic optimization algorithm named grey wolf optimization algorithm based on leadership and hunting behavior of grey wolf for the reactive power planning of a connected power network. The performance of the proposed opposition‐based grey wolf optimization (OGWO) and grey wolf optimization (GWO) is examined and tested successfully in standard IEEE 14, IEEE 30, and IEEE 57 bus test systems for the minimization of active power loss and the total operating cost while maintaining voltage profile of the buses within permissible limit. Active power loss and total operating cost are minimized by optimal planning of the reactive generation of generators, transformer tap setting arrangements, and reactive output of shunt capacitors as installed at weak nodes under different loading conditions. The weak nodes are detected by power flow analysis. The results obtained by GWO algorithm is compared to other popular techniques recently reported in recent state‐of‐the‐art literature. It is observed that proposed OGWO and GWO algorithm yields much better result in terms of reducing operating cost and minimizing active power loss. Merit lies with GWO is that its simple structure for implementation and its ability not to be trapped in local minima, thus exploring wider search area. [ABSTRACT FROM AUTHOR]

In the present work, reactive power planning problem along with voltage stability margin is addressed by effective co-ordination of reactive power sources. Modal analysis and L-index methods are used to detect weak nodes of the system accordingly. Differential Evolution (DE) and Genetic Algorithm (GA)-based optimization techniques are applied for the proper co-ordination of Var sources under base and increased loading conditions maintaining voltage stability of the connected power network. The problem is multi-objective and IEEE 30 bus system is taken as the standard system. It is observed that modal analysis based detection of weak nodes are more effective than the L-index-based detection. Moreover, the DE-based optimization algorithm gives better result compared to GA-based approach in maximizing reactive power reserves. [ABSTRACT FROM AUTHOR]

Reactive power planning is one of the most challenging problem for efficient and source operation of an interconnected power network. It requires effective and optimum co-ordination of all the reactive power sources present in the network. Recently, Teaching Learning Based Optimization (TLBO) algorithm is evolved and finds its application in the field of engineering optimization. In the proposed work TLBO based optimization algorithm is used for reactive power planning and applied in IEEE 30 and IEEE 57 bus system. The results obtained by this method are compared with the results obtained by other optimization techniques like PSO (Particle swarm optimization), Krill heard, HSA (Harmony search algorithm) and BB-BC (Big Bang-Big Crunch). At the end, TLBO appears as the most effective method for reactive power planning among all the methods discussed and can be considered as one of the standard method for reactive power optimization. [ABSTRACT FROM AUTHOR]

In the proposed work, authors have applied swarm intelligence based algorithms for the effective Co-ordination of Flexible AC transmission system (FACTS) devices with other existing Var sources present in the network. IEEE 30 and IEEE 57 bus systems are taken as standard test systems. SPSO (Simple Particle Swarm Optimization) and other two swarm based intelligence approaches like APSO (Adaptive Particle Swarm Optimization) and EPSO (Evolutionary Particle Swarm Optimization) are used for the optimal setting of the Var sources and FACTS devices. The result obtained with the proposed approach is compared with the result found by the conventional RPP (Reactive power planning) approach where shunt capacitors, transformer tap setting arrangements and reactive generations of generators are used as planning variables. It is observed that reactive power planning with FACTS devices yields much better result in terms of reducing active power loss and total operating cost of the system even considering the investment costs of FACTS devices. [ABSTRACT FROM AUTHOR]

In the present work, GSA (gravitational search algorithm) based optimization algorithm is applied for the optimal allocation of FACTS devices in transmission system. IEEE 30 & IEEE 57 test bus systems are taken as standards. Both active and reactive loading of the power system is considered and the effect of FACTS devices on the power transfer capacity of the individual generator is investigated. The proposed approach of planning of reactive power sources with the FACTS devices is compared with other globally accepted techniques like GA (Genetic Algorithm), Differential Evolution (DE), and PSO (Particle Swarm Optimization). From the results obtained, it is observed that incorporating FACTS devices, loadability of the power system increases considerably and each generator present in the system is being able to dispatch significant amount of active power under different increasing loading conditions where the steam flow rate is maintained corresponding to the base active loading condition. The active power loss & operating cost also reduces by significant margin with FACTS devices at each loading condition and GSA based planning approach of reactive power sources with FACTS devices found to be the best among all the methods discussed in terms of reducing active power loss and total operating cost of the system under all active and reactive loading situations. [ABSTRACT FROM AUTHOR]

Reactive power source planning problem has significant role for secure and economic operation of power system. Reactive power planning problem is nothing but the proper coordination of existing Var sources which lead to loss minimization and cost economic operation of the system. In the proposed approach several bio-inspired algorithms like Particle Swarm Optimization (PSO), Evolutionary Particle Swarm Optimization (EPSO), Adaptive Particle Swarm Optimization (APSO), Hybrid Particle Swarm Optimization (HPSO) and Bacterial Foraging Algorithm (BFA) are used for the reactive power planning problem. Finally a comparison of all these techniques are made on the basis of the results obtained when applied to different standard test system. [ABSTRACT FROM AUTHOR]