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The article presents a solution to detect rotor position at stand still condition for all types of permanent magnet brushless dc motors. The solution provides both secure and fast method for starting of the brushless motor, that is independent on the sensorless control scheme used. Nonlinearities found in standard three phase permanent magnet dc motor are used to derive the rotor position at stand still. The described solution assumes that there is availability of the neutral point of the three phase star motor windings.
The small and remote households in Northern regions demand thermal energy rather than electricity. Wind turbine in such places can be used to convert wind energy into thermal energy directly using a heat generator based on the principle of the Joule machine. The heat generator driven by a wind turbine can reduce the cost of energy for heating system. However the optimal performance of the system depends on the torque-speed characteristics of the wind turbine and the heat generator. To achieve maximum efficiency of operation both characteristics should be matched. In the article the condition of optimal performance is developed and an example of the system operating at maximum efficiency is simulated.
This paper presents a new method of analysing the error of a sampled-data velocity stabilising system with a wide range of pulse width modulation. The analysis is based on multi-channel model obtained as a result of approximation of pulse-modulated signal at the output of a PWM converter. Approximation of piecewise-linear modulation characteristics of each channel has been obtained as a series expansion of Hermite polynomials where the expansion comprises two polynomials of the first and third orders. The transfer function of every channel and a closed-loop system has been obtained using multidimensional Z-transform. The analytical expression of an error under impact of a step input has been derived using a transfer function of the closed-loop system. A dc electric drive has been used as an example of high accuracy sample-data stabilising system to verify and demonstrate the proposed method.
Error analysis in a high accuracy sampled-data velocity stabilising system using Volterra series
(2015)
This paper provides a performance analysis of a wearable photovoltaic system mounted on the outer surface of a backpack. Three types of photovoltaic materials, commonly used for electricity generation, have been investigated under various conditions including sun irradiance, angle-of-incidence and sun inclination. The results of the investigation have shown that the system equipped with the rigid mono-Si panels performs 3.5 to 4.9 times better than the system equipped with a-Si flexible PV modules. The average power generated by the wearable photovoltaic system is about 30% of the maximum installed power for any photovoltaic type. This paper presents the test data resulting from the evaluation of the daily energy production of a wearable photovoltaic power supply.
The paper presents a new control strategy of management of transport companies operating in completive transport environment. It is aimed to optimise the headway of transport companies to provide the balance between costs and benefits of operation under competition. The model of transport system build using AnyLogic comprises agent-based and discrete-event techniques. The model combined two transport companies was investigated under condition of the competition between them. It was demonstrated that the control strategy can ensure the balance of interests of transport companies trying to find compromise between cost of operation and quality of service.
This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.
The paper analyses a random pulse width modulated technique applicable for Z-source inverter. The proposed technique comprises the random pulse position PWM with a randomised frequently triangular carrier having characteristics of both space vector and random lead-lag modulations. This random pulse PWM is produced through the logical comparison of the pseudorandom binary sequence bits with the PWM pulses corresponding to two fixed frequency triangular carriers. Matlab/Simulink was used for modelling and simulation of the proposed method. It has been found that the output harmonic spectra of a Z-source inverter operating under the PWM with randomised frequency are dispersed and continuously distributed that can significantly reduce acoustic noise generated by the inverter and the load.
Predefined heater parameters are involved in self-tuned temperature control for plastic moulding. However the heater system transfer function depends on many external parameters, such as barrel filling level, type of plastic etc. This paper discusses a recursive least-square estimation of plastic moulding heater parameters identification. The real heaters have been estimated by recursive least-square method as 2nd or 3rd order transfer function having an error less than 7.5%. The optimal sampling time for the identification process of different heater cartridges has been obtained from Matlab simulation. The parameters of estimated model can be used in self-tuned temperature controllers for injection plastic moulding heater.
Wireless sensor networks are widely used in a variety of fields including industrial environments. In case of a clustered network the location of cluster head affects the reliability of the network operation. Finding of the optimum location of the cluster head, therefore, is critical for the design of a network. This paper discusses the optimisation approach, based on the brute force algorithm, in the context of topology optimisation of a cluster structure centralised wireless sensor network. Two examples are given to verify the approach that demonstrate the implementation of the brute force algorithm to find an optimum location of the cluster head.
Power train models are required to simulate hence predict energy consumption of vehicles. Efficiencies for different components in power train are required. Common procedures use digitalised shell models (or maps) to model the efficiency of Internal Combustion Engines (ICE) and manual gearboxes (MG). Errors are connected with these models and affect the accuracy of the calculation. The accuracy depends on the configuration of the simulation, the digitalisation of the data and the data used. This paper evaluates these sources of error. The understanding of the source of error can improve the results of the modelling by more than eight percent.
This paper proposes a new artificial neural network-based position controller for a full-electric injection moulding machine. Such a controller improves the dynamic characteristics of the positioning for hot runners, pin valve and the injection motors for varying moulding parameters. Practical experimental data and Matlab’s System Identification Toolbox have been used to identify the transfer functions of the motors. The structure of the artificial neural network, which used positioning error and speed of error, was obtained by numerical modelling in Matlab/Simulink. The artificial neural network was trained using back-propagation algorithms to provide control of the motor current thus ensuring the required position and velocity. The efficiency of the proposed ANN-based controller has been estimated and verified in Simulink using real velocity data and the position of the injection moulding machine and pin valve motors.
This paper proposes an approach to an ANN-based temperature controller design for a plastic injection moulding system. This design approach is applied to the development of a controller based on a combination of a classical ANN and integrator. The controller provides a fast temperature response and zero steady-state error for three typical heaters (bar, nozzle, and cartridge) for a plastic moulding system. The simulation results in Matlab Simulink software and in comparison to an industrial PID regulator have shown the advantages of the controller, such as significantly less overshoot and faster transient (compared to PID with autotuning) for all examined heaters. In order to verify the proposed approach, the designed ANN controller was implemented and tested using an experimental setup based on an STM32 board.
