The increasing penetration of renewable energy resources in power systems has improved microgrid's implementation. A microgrid is a localized grouping of electricity sources and loads that normally operates connected to and synchronous with the traditional centralized grid but can disconnect and function autonomously as physical and economic conditions dictate. Main factors to control in a microgrid are mainly frequency and voltage regulation, energy management, operation, and control scheme. This high penetration of renewable generation systems with their intermittent nature and unpredictable output power fluctuations might cause many control problems and large frequency/voltage deviation in microgrid stand-alone operation. Thus, to maintain the microgrid stability, an efficient and highly reliable control scheme is required. In this paper, a newly proposed fuzzy logic-based robust control mechanism is used to stabilize the frequency and direct current bus voltages in large fluctuations caused by sudden changes in power generation or load side. Also, supercapacitor and battery energy storage system are used to stabilize direct current bus voltages. This proposed method ensures the system efficiency and stability by reducing the system complexity and transient time, minimizing the frequency deviations, and preventing synchronous generator units from surpassing their power ratings in response to these disruptions. Simulation results also validate the effectiveness of the proposed controller in comparison with previous techniques. KEYWORDS battery energy storage system (BESS), frequency stabilization, fuzzy logic technique, microgrid (MG), renewable generation, super capacitor
In this work, an advanced drone battery charging system is developed. The system is composed of a drone charging station with multiple power transmitters and a receiver to charge the battery of a drone. A resonance inductive coupling-based wireless power transmission technique is used. With limits of wireless power transmission in inductive coupling, it is necessary that the coupling between a transmitter and receiver be strong for efficient power transmission; however, for a drone, it is normally hard to land it properly on a charging station or a charging device to get maximum coupling for efficient wireless power transmission. Normally, some physical sensors such as ultrasonic sensors and infrared sensors are used to align the transmitter and receiver for proper coupling and wireless power transmission; however, in this system, a novel method based on the hill climbing algorithm is proposed to control the coupling between the transmitter and a receiver without using any physical sensor. The feasibility of the proposed algorithm was checked using MATLAB. A practical test bench was developed for the system and several experiments were conducted under different scenarios. The system is fully automatic and gives 98.8% accuracy (achieved under different test scenarios) for mitigating the poor landing effect. Also, the efficiency η of 85% is achieved for wireless power transmission. The test results show that the proposed drone battery charging system is efficient enough to mitigate the coupling effect caused by the poor landing of the drone, with the possibility to land freely on the charging station without the worry of power transmission loss.
Abstract:The microgrid (MG) concept is attracting considerable attention as a solution to energy deficiencies, especially in remote areas, but the intermittent nature of renewable sources and varying loads cause many control problems and thereby affect the quality of power within a microgrid operating in standalone mode. This might cause large frequency and voltage deviations in the system due to unpredictable output power fluctuations. Furthermore, without any main grid support, it is more complex to control and manage the system. In past, droop control and various other coordination control strategies have been presented to stabilize the microgrid frequency and voltages, but in order to utilize the available resources up to their maximum capacity in a positive way, new and robust control mechanisms are required. In this paper, a standalone microgrid is presented, which integrates renewable energy-based distributed generations and local loads. A fuzzy logic-based intelligent control technique is proposed to maintain the frequency and DC (direct current)-link voltage stability for sudden changes in load or generation power. Also from a frequency control perspective, a battery energy storage system (BESS) is suggested as a replacement for a synchronous generator to stabilize the nominal system frequency as a synchronous generator is unable to operate at its maximum efficiency while being controlled for stabilization purposes. Likewise, a super capacitor (SC) and BESS is used to stabilize DC bus voltages even though maximum possible energy is being extracted from renewable generated sources using maximum power point tracking. This newly proposed control method proves to be effective by reducing transient time, minimizing the frequency deviations, maintaining voltages even though maximum power point tracking is working and preventing generators from exceeding their power ratings during disturbances. However, due to the BESS limited capacity, load switching (load shedding scheme) as last option is also introduced in this paper. Simulation results prove the effectiveness of the proposed control strategy from both frequency and voltage perspectives.
Recently, electrical drives generally associate inverter and induction machine. Therefore, inverter must be taken into consideration along with induction motor in order to provide a relevant and efficient diagnosis of these systems. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduces overall efficiency. In this paper, fault detection and diagnosis based on features extraction and neural network technique for three-phase inverter is presented. Basic purpose of this fault detection and diagnosis system is to detect single or multiple faults efficiently. Several features are extracted from the Clarke transformed output current and used in neural network as input for fault detection and diagnosis. Hence, some simulation study as well as hardware implementation and experimentation is carried out to verify the feasibility of the proposed scheme. Results show that the designed system not only detects faults easily, but also can effectively differentiate between multiple faults. These results prove the credibility and show the satisfactory performance of designed system. Results prove the supremacy of designed system over previous feature extraction fault systems as it can detect and diagnose faults in a single cycle as compared to previous multicycles detection with high accuracy.
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