Adaptif Poly Frame PRMA pada Jaringan M2M Kognitif Kapiler

Komunikasi mesin ke mesin (M2M) pada jaringan kapiler, menggunakan metode transmisi Packet Reservation Multiple Access (PRMA), dan struktur frame data frame biasa, serta skenario komunikasi event driven. Seiring dengan pertambahan perangkat, metode, struktur frame dan skenario komunikasi tersebut tidak dapat menangani laju data yang sangat banyak, sehingga terjadi kemacetan yang memperlambat komunikasi. Penelitian ini bertujuan membuat komunikasi M2M yang lancar walaupun perangkat bertambah banyak, dengan membuat struktur frame baru dan skenario komunikasi baru, berupa Adaptive Poly Frame (APF) serta Scheduler Update (SU). APF dan SU dirancang dengan memberikan nomor urut serta prioritas pada data, yang kemudian dioptimasi dengan meningkatkan peluang persaingan MK (O), jumlah siklus huni slot (B), jumlah siklus huni kanal (S), dan Transmisi Sukses PRMA (TSPRMA). Penelitian ini menghasilkan transmisi sukses 92-28%, optimasi transmisi sukses 93-30%, siklus transmisi 1,5-8,1% dan reduksi siklus transmisi 0,9-7,2%.AbstractMachine to machine (M2M) communication in capillary networks, using the Packet Reservation Multiple Access (PRMA) transmission method, and ordinary frame data frame structures, as well as event driven communication scenarios. Along with the addition of devices, methods, frame structures and communication scenarios cannot handle very large data rates, resulting congestion that results in inefficient communication. This research aims to make M2M communication efficient even though the device is multiplying, by creating new frame structures and new communication scenarios, in the form of Adaptive Poly Frame (APF) and Scheduler Update (SU). APF and SU are designed by sequence number and prioritizing data, which is then optimized by increase the chance of MK contestation (O), the number of slot occupancy cycles (B), the number of canal occupancy cycles (S) and PRMA Success Transmission (TSPRMA). This research resulted in 92-28% successful transmission, 93-30% successful transmission optimization, 1.5-8.1% transmission cycle and 0.9-7.2% transmission cycle reductio

Cooperative Strategies for Management of Power Quality Problems in Voltage-Source Converter-based Microgrids

The development of cooperative control strategies for microgrids has become an area of increasing research interest in recent years, often a result of advances in other areas of control theory such as multi-agent systems and enabled by emerging wireless communications technology, machine learning techniques, and power electronics. While some possible applications of the cooperative control theory to microgrids have been described in the research literature, a comprehensive survey of this approach with respect to its limitations and wide-ranging potential applications has not yet been provided. In this regard, an important area of research into microgrids is developing intelligent cooperative operating strategies within and between microgrids which implement and allocate tasks at the local level, and do not rely on centralized command and control structures. Multi-agent techniques are one focus of this research, but have not been applied to the full range of power quality problems in microgrids. The ability for microgrid control systems to manage harmonics, unbalance, flicker, and black start capability are some examples of applications yet to be fully exploited. During islanded operation, the normal buffer against disturbances and power imbalances provided by the main grid coupling is removed, this together with the reduced inertia of the microgrid (MG), makes power quality (PQ) management a critical control function. This research will investigate new cooperative control techniques for solving power quality problems in voltage source converter (VSC)-based AC microgrids. A set of specific power quality problems have been selected for the application focus, based on a survey of relevant published literature, international standards, and electricity utility regulations. The control problems which will be addressed are voltage regulation, unbalance load sharing, and flicker mitigation. The thesis introduces novel approaches based on multi-agent consensus problems and differential games. It was decided to exclude the management of harmonics, which is a more challenging issue, and is the focus of future research. Rather than using model-based engineering design for optimization of controller parameters, the thesis describes a novel technique for controller synthesis using off-policy reinforcement learning. The thesis also addresses the topic of communication and control system co-design. In this regard, stability of secondary voltage control considering communication time-delays will be addressed, while a performance-oriented approach to rate allocation using a novel solution method is described based on convex optimization