Efficient control of industrial delay processes is a challenging problem in the field of process control. Time delays are generally experienced in industrial processes from distance velocity lags, composition analysis loops, recycle time, mass, and energy transportation time. A high time delay adds a large phase lag to the system, thereby affecting the closed-loop control system stability and thus not easily controlled with PID approach. Smith predictor (SP) is a prominent technique based on process model for processes with high time delay. Unfortunately, the performance of SP deteriorates when the plant model is inaccurate. To overcome the problems related to conventional SP, various modifications have been suggested over the years in terms of structure alterations and controller parameters tuning improvements. This paper focuses on a comparative study of various Smith predictor configurations available in the literature for controlling inverse, integrating, stable and unstable industrial processes with time delay.

The fault detection and isolation of generators used in wind turbines gathering interest as to maximize the reliability and avail of distributed energy systems with recent unmatched growth in construction of offshore wind farms. In particular it is interested in performing fault detection and isolation (FDI) of incipient faults affecting the measurements of the three-phase signals (currents) in a controlled DFIG and PMSG. Although different authors have dealt with FDI for sensors in induction machines and in DFIGs, most of them rely on the machine model with constant parameters. However, the parameter uncertainties due to changes in the operating conditions will produce degradation in the performance of such FDI systems. The robust techniques to detect faults are exist but there is a need of extra sensor. This paper proposed a systematic methodology for the design of sensor FDI systems with the following characteristics: i) capable of detecting and isolating incipient additive (bias) faults, ii) robust against changes in the references/disturbances affecting the controlled DFIG and PMSG as well as modeling/parametric uncertainties, iii) residual generation system based on a multi-observer strategy to enhance the isolation process, The designed sensor FDI systems have been validated using measured voltages, as well as simulated data from a controlled DFIG. First the state space models of DFIG and PMSM explained followed by kalman filter introduction and current sensor fault detection using a bank of kalman filter named dedicated Observer Scheme and generalized Observer scheme to detect simultaneous and multiple faults was theorized and simulated using MATLAB simulation tool .The simulation results were summarized with and without Sensor fault

Proper utilization of the available low-power is essential to extend the lifetime of the battery-operated wireless sensor networks (WSNs) for environmental monitoring applications. It is mandatory because the batteries cannot be replaced or recharged after deployment due to impracticality. To utilize the power properly, an appropriate cluster-based data gathering algorithm is needed which reduces the overall power consumption of the network significantly. So, in this paper, a grid-based data gathering algorithm called energy-efficient structured clustering algorithm with relay (EESCA-WR) is proposed. In this algorithm, the grids have a single grid leader (GL) and multiple grid relays (GRs). The count of GRs in a grid is variable based on the geographic location of the grid with respect to the destination sink (DS). By doing this, we ensure that the reduction in power consumption is achieved because of the multi-hop short-distance data communications. Also, the GLs are rotated in the right intervals in hybrid modes to minimize the usage of control messages considerably. A hybrid GL selection policy, a threshold-based GL rotation policy, and the policy of allotting dedicated relay-clusters in every grid make the proposed algorithm unique and better for homogeneous and heterogeneous wireless sensor networks. Performance evaluation of the proposed algorithm is carried out by varying the length of the field, the node-density, the grid-count, and the initial energy. Experimental results show that EESCA-WR is extremely scalable, energy-efficient with a minimum number of control messages, and can be used for large scale WSNs.

Wireless communication is preferred in numerous sensing applications due to its convenience, cost‐effectiveness, and flexibility. Modern sensors are versatile to sense the environmental factors and send them wirelessly. The information collection centres prefer to collect confined clustered information from a group of sensors rather than collecting them from individual sensors. Good connectivity, speedy communication, and effective data gathering can be ensured in the network when a good clustering algorithm is utilized. In this paper, a simple and effective clustering algorithm called energy efficient structured clustering algorithm (EESCA) is proposed for the environmental monitoring fields. Cluster heads (CHs) are elected based on average communication distance and lingering energy. Further, a new parameter called cluster head to normal ratio (CTNR) is introduced to rotate the cluster head role among the nodes. The performance evaluation is carried out in terms of first node die (FND), simulation time, scalability, load balancing, and a new parameter called complete useful data percentage (CUDP). Simulations are conducted for three different network scenarios. Results are compared with the renowned existing algorithms low energy adaptive clustering hierarchy (LEACH) and scalable energy efficient clustering hierarchy (SEECH) and it is proved that the proposed technique is beneficial for WSNs.