In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural n...In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural network and adaptive neurofuzzy inference system(ANFIS)controller with safe boundary algorithm.In this method of target seeking behaviour,the obstacle avoidance at every instant improves the performance of robot in navigation approach.The inputs to the controller are the signals from various sensors fixed at front face,left and right face of the AMR.The output signal from controller regulates the angular velocity of both front power wheels of the AMR.The shortest path is identified using fuzzy,neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation.The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation,in particular with curved/irregular obstacles.展开更多
This paper proposes a novel controllable crowbar based on fault type(CBFT)protection technique for doubly fed induction generator(DFIG)wind energy conversion system connected to grid.The studied system consists of six...This paper proposes a novel controllable crowbar based on fault type(CBFT)protection technique for doubly fed induction generator(DFIG)wind energy conversion system connected to grid.The studied system consists of six DFIG wind turbines with a capacity of 1.5 MW for each of them.The operation mechanism of proposed technique is used to connect a set of crowbar resistors in different connection ways via activation of controllable circuit breakers(CBs)depending on the detected fault type.For each phase of DFIG,a crowbar resistor is connected in parallel with a controllable CB and all of them are connected in series to grid terminals.The adaptive neuro-fuzzy inference system(ANFIS)networks are designed to detect the fault occurrence,classify the fault type,activate the CBs for crowbar resistors associated with faulted phases during fault period,and deactivate them after fault clearance.The effectiveness of proposed CBFT protection technique is investigated for different fault types such as symmetrical and unsymmetrical faults taking into account the single-phase to ground fault is the most frequently fault type that occurs in power systems.Also,a comparison between the behaviours of studied system in cases of using traditional parallel rotor crowbar,classical outer crowbar,and proposed CBFT protection techniques is studied.The fluctuations of DC-link voltage,active power,and reactive power for studied system equipped with different protection techniques are investigated.Moreover,the impacts of different crowbar resistance values on the accuracy of proposed technique are studied.The simulation results show that,the proposed technique enhances the stability of studied wind turbine generators and contributes in protection of their components during faults.展开更多
文摘In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural network and adaptive neurofuzzy inference system(ANFIS)controller with safe boundary algorithm.In this method of target seeking behaviour,the obstacle avoidance at every instant improves the performance of robot in navigation approach.The inputs to the controller are the signals from various sensors fixed at front face,left and right face of the AMR.The output signal from controller regulates the angular velocity of both front power wheels of the AMR.The shortest path is identified using fuzzy,neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation.The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation,in particular with curved/irregular obstacles.
文摘This paper proposes a novel controllable crowbar based on fault type(CBFT)protection technique for doubly fed induction generator(DFIG)wind energy conversion system connected to grid.The studied system consists of six DFIG wind turbines with a capacity of 1.5 MW for each of them.The operation mechanism of proposed technique is used to connect a set of crowbar resistors in different connection ways via activation of controllable circuit breakers(CBs)depending on the detected fault type.For each phase of DFIG,a crowbar resistor is connected in parallel with a controllable CB and all of them are connected in series to grid terminals.The adaptive neuro-fuzzy inference system(ANFIS)networks are designed to detect the fault occurrence,classify the fault type,activate the CBs for crowbar resistors associated with faulted phases during fault period,and deactivate them after fault clearance.The effectiveness of proposed CBFT protection technique is investigated for different fault types such as symmetrical and unsymmetrical faults taking into account the single-phase to ground fault is the most frequently fault type that occurs in power systems.Also,a comparison between the behaviours of studied system in cases of using traditional parallel rotor crowbar,classical outer crowbar,and proposed CBFT protection techniques is studied.The fluctuations of DC-link voltage,active power,and reactive power for studied system equipped with different protection techniques are investigated.Moreover,the impacts of different crowbar resistance values on the accuracy of proposed technique are studied.The simulation results show that,the proposed technique enhances the stability of studied wind turbine generators and contributes in protection of their components during faults.
