A Novel Higher Level Symmetrical and Asymmetrical Multilevel Inverter for Solar Energy Environment

Abstract

Model-based reinforcement learning uses models to plan, where the predictions and policies of an agent can be improved by using more computation without additional data from the environment, thereby improving sample efficiency. However, learning accurate estimates of the model is hard. Subsequently, the natural question is whether we can get similar benefits as planning with modelfree methods. Experience replay is an essential component of many model-free algorithms enabling sample-efficient learning and stability by providing a mechanism to store past experiences for further reuse in the gradient computational process. Prior works have established connections between models and experience replay by planning with the latter. This involves increasing the number of times a mini-batch is sampled and used for updates at each step (amount of replay per step). We attempt to exploit this connection by doing a systematic study on the effect of varying amounts of replay per step in a well-known modelfree algorithm: Deep Q-Network (DQN) in the Mountain Car environment. We empirically show that increasing replay improves DQN's sample efficiency, reduces the variation in its performance, and makes it more robust to change in hyperparameters. Altogether, this takes a step toward a better algorithm for deployment.

A switched-capacitor and floating-capacitor (SCFC) based multilevel boost inverter with a single DC power supply and sensor-less voltage balancing method

“…Also, they have no voltage-boosting capability. Topologies in [12,24] require an end-side H-bridge converter to get negative voltage levels. In [27,29], switches have to suffer voltage stress four times the input voltage.…”

Development of fault‐tolerant reduced device version with switched‐capacitor based multilevel inverter topologies

“…To generate “ N ” level output, the LS‐PWM strategy requires, “ N‐1 ” triangular carrier signals with switching (Carrier) frequency of ( f S = 3150 Hz ). The carrier waves are compared with reference modulating sinusoidal signal ( f m = 50 Hz ) to obtain required gate pulses 6 . The modulation control strategy is shown in Figure 8.…”

“…The most important characteristic of MLI is its high degree of scalability, which allows high‐voltage upgradation using medium‐voltage power switches. Due to the requirement of independent dc sources, the MLI implementation is apt to solar, fuel cells, and wind energy systems, though their outputs are dependent on environmental conditions 5,6 . Figure 1 shows renewable energy technologies with DC/AC generation for their implementation in subsequent MLI topologies.…”

Development of Open Switch Fault-Tolerant Capability in CCS-MLI Topology

“…Fault-tolerant MLIs require multiple switching states capable of realizing the same level of inverter output. Such multiple switching states in the literature are termed "redundant states" [10]. The RDC-MLI would certainly be less likely to become a full fault-tolerant topology due to the availability of fewer "redundant states".…”

“…Recent topologies proposed for fault-tolerance (FT) in the RDC-MLI configuration [10,11] are not suitable for capacitor charge balance due to their symmetric shape. The topologies presented in [12,13] propose zero use of the DC sources associated with faulty cells.…”