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<p>Generally, for remote places extension of grid is uneconomical and difficult. Off-grid hybrid power systems (OGHPS) has  renewable energy sources integrated with conventional sources. OGHPS is very significant as it is the only source of electric supply for remote areas. OGHPS under study  has Induction generator (IG) for wind power generation, Photo-Voltaic source with inverter, Synchronous generator (SG) for Diesel Engine (DE) and load. Over-rated PV-inverter has capacity to supply reactive power.  SG of  DE  has Automatic voltage regulator for excitation control to regulate terminal voltage. Load and IG demands reactive power, causes reactive power imbalance hence voltage fluctuations in OGHPS. To manage reactive power for voltage control, two control structures with Proportional–Integral controller(PI), to control  inverter reactive power and  SG excitation by automatic voltage regulator are incorporated.  Improper tuning of controllers lead  to oscillatory and sluggish response. Hence in this test system both controllers need to be tune optimally. This paper proposes novel intelligent computing algorithm , Enhanced Bacterial forging algorithm (EBFA) for optimal reactive power controller for voltage control in OGHPS. Small signal model of OGHPS with proposed controller is  tested for different disturbances. simulation results  are compared  with conventional  method , proved the effectiveness of EBFA.</p>

Hybrid off-grid power systems with different renewable and non-renewable energy sources, such as wind, photovoltaics, and diesel generation, have a wide application scope in regions where grid extension is not possible. To supply quality power, hybrid off-grid power systems need proper reactive power management to deal with randomly changing load and supply. In particular, to realize dependable and quality power supply, hybrid off-grid power systems require suitable and efficient control techniques. A properly tuned controller of reactive power sources is crucial to maintain a prescribed voltage profile. Computational intelligence techniques such as particle swarm optimization can provide desired and acceptable solutions for optimization problems. In this study, we applied computational intelligent techniques for optimal control of reactive power sources, such as photovoltaic inverters and automatic voltage regulators for synchronous generators in diesel engines, to investigate dynamic voltage profile stability through reactive power management.