DC-DC boost converters are necessary to extract power from solar panels. The output voltage from these panels is far lower than the utility voltage levels. One of the main functions of the boost converter is to provide a considerable step-up gain to interface the panel to the utility lines. There are several techniques used to boost the low panel voltage. Some of the issues faced by these topologies are a high duty ratio operation, complex design with multiple active switches and discontinuous input current that affects the power drawn from the panel. This paper presents a boost converter topology that combines the advantages of an interleaved structure, a voltage lift capacitor and a passive voltage multiplier network. A mathematical analysis of the proposed converter during its various modes of operation is presented. A 100 W prototype of the proposed converter is designed and tested. The prototype is controlled by a PIC16F18455 microcontroller. The converter is capable of achieving a gain of 10 without operating at extremely high duty ratios. The voltage stress of the switch is far lower than the maximum output voltage.

A digital H∞ controller for a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS) is presented. Wind energy is an uncertain fluctuating resource which requires a tight control management. So, it is still an exigent task for the control design engineers. The conventional proportional-integral (PI) control is not ideal during high turbulence wind velocities, and the nonlinear behavior of the power converters. These are raising interest towards the robust control concepts. The robust design is to find a controller, for a given system, such that the closed-loop system becomes robust that assurance high-integrity and fault tolerant control system, robust H∞ control theory has befallen a standard design method of choice over the past two decades in industrial control applications. The robust H∞ control theory is also gaining eminence in the WECS. Due to the implementation complexity for the continuous H∞ controller, and availability of the high speedy micro-controllers, the design of a sample-data or a digital H∞ controller is very important for the realistic implementation. But there isn’t a single research to evaluate the performance of the digital H∞ controller for the WECS. In this paper, the proposed digital H∞ controller schemes comprise for the both generator and grid interactive power converters, and the control performances are compared with the conventional PI controller and the fuzzy controller. Simulation results confirm the efficacy of the proposed method Energies 2013, 6 2085 which are ensured the WECS stabilities, mitigate shaft stress, and improving the DC-link voltage and output power qualities.

Robust control methodology for two-area load frequency control model is proposed in this paper. The paper presents a comparative study between the performance of model predictive controller (MPC) and optimized proportional–integral–derivative (PID) controller on different systems. An objective function derived from settling time, percentage overshoot and percentage undershoot is minimized to obtain the gains of the PID controller. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to tune the parameters of the PID controller through performance optimization of the system. System performance characteristics were compared to another controller designed based on MPC. Detailed comparison was performed between the performances of the MPC and optimized PID. The effectiveness and robustness of the proposed schemes were verified by the numerical simulation in MATLAB environment under different scenarios such as load and parameters variations. Moreover, the pole-zero map of each proposed approach is presented to investigate their stability.

In this article, a mathematical model of ultrasonic motors for speed control, which includes the non-linearity and approximation of the dead-zone in control input, is presented. The intrinsic properties of ultrasonic motors (high torque for low speed, high static torque, compact in size, etc.) offer great advantages for industrial applications. However, the speed property of ultrasonic motors has a strong non-linear property as well as the dead-zone effect in control input associated with load torque. These properties present serious problems for accurate speed control. This article presents a mathematical model represented by a sinusoidal function, which includes these properties. The model is simple and has good approximation of the non-linearity and time varying properties. The proposed speed model has been validated by experimental results.

Price forecasting has become a very valuable tool in the current upheaval of electricity market deregulation. It plays an important role in power system planning and operation, risk assessment and other decision making. This paper provides a method for predicting hourly prices in the day-ahead electricity market using recursive neural network (RNN) technique, which is based on similar days approach. RNN is a multi-step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. In this way, it is carried out recursively for twenty four steps to predict next 24 hour prices. Comparison of forecasting performance of the proposed RNN model with similar days along with other literature is presented. The proposed method is examined on the PJM electricity market. The results obtained through the simulation show that the proposed RNN model can provide efficient, accurate and better results.

Short-term price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk. This paper adopts artificial neural network (ANN) model based on similar days methodology in order to forecast weekly electricity prices in the PJM market. To demonstrate the superiority of the proposed model, extensive analysis is conducted using data from the PJM interconnection. The factors impacting the electricity price forecasting, including time factors, load factors, and historical price factors are discussed. Comparison of forecasting performance of the proposed ANN model with that of forecasts obtained from similar days approach is discussed. The forecasting error is the major concern for forecaster; a lower error indicates a better result. Accumulative error depends on forecasting period (hourly, daily, weekly, monthly, etc.). It will increase for a longer time forecasts. In this paper, the test results obtained by using the proposed ANN provide reliable forecast for weekly price forecasting as the mean absolute percentage error (MAPE) values obtained for the first and last week of February 2006 are 7.66 % and 8.88%, respectively. Similarly, MAPE for the second week of January 2006 is obtained as 12.92%. Forecast mean square error (FMSE) and MAPE results obtained through the simulation show that the proposed ANN model is capable of forecasting locational marginal price (LMP) in the PJM market efficiently.