Liuchen Chang

An intelligent maximum power extraction algorithm for inverter-based variable speed wind turbine systems

This paper focuses on the development of maximum wind power extraction algorithms for inverter-based variable speed wind power generation systems. A review of existing maximum wind power extraction algorithms is presented in this paper, based on which an intelligent maximum power extraction algorithm is developed by the authors to improve the system performance and to facilitate the control implementation. As an integral part of the max-power extraction algorithm, advanced hill-climb searching method has been developed to take into account the wind turbine inertia. The intelligent memory method with an on-line training process is described in this paper. The developed maximum wind power extraction algorithm has the capability of providing initial power demand based on error driven control, searching for the maximum wind turbine power at variable wind speeds, constructing an intelligent memory, and applying the intelligent memory data to control the inverter for maximum wind power extraction, without the need for either knowledge of wind turbine characteristics or the measurements of mechanical quantities such as wind speed and turbine rotor speed. System simulation results and test results have confirmed the functionality and performance of this method.

A novel demand side management program using water heaters and particle swarm optimization

Power systems operators have the task of maintaining the balance between the demand and generation of electric power. Much research and attention is being given to find more environmental friendly sources of power generation. Naturally, more power is required when the load is at its peak value, and this tends to be when the most non environmentally friendly sources of power generation are used. This paper proposes a new controller for peak load shaving by intelligently scheduling power consumption of domestic electric water heater using binary particle swarm optimization. Past studies show that similar demand side management programs were not successful because the impact that the load control has on the end users comfort. In this study, Binary Particle Swarm Optimization (BPSO) finds the optimal load demand schedule for minimizing the peak load demand while maximizing customer comfort level. A simulation in Matlab is used to test the performance of the demand response program using field data gathered by smart meters from 200 households. The direct load control is shown to be an effective tool for peak shaving of load demand, shifting the loads to valleys and reducing the aggregated load of electricity without compromising customer satisfaction.

Wavelet-Based Reactive Power and Energy Measurement in the Presence of Power Quality Disturbances

This paper investigates the performance of electronic reactive power/energy meters by evaluating differences that arise as a result of (1) the distinct operating principles of the meter and (2) the presence of stationary and nonstationary power quality disturbances. The paper also includes a comparative study to determine the effectiveness of using orthogonal, bi-orthogonal, or reverse bi-orthogonal wavelets for such application. Moreover, the paper identifies the reactive power quantities required for accurate assessment and monitoring of reactive energy in the presence of power quality disturbances. Finally, the paper provides recommendations on reactive power and energy measurement for future generations of smart meters.

Awater heater model for increased power system efficiency

This paper presents a domestic hot water heater model to be used in a demand side management program. Water heater loads are extracted from household load data, and then used do develop household water usage data. The model incorporates both the thermal losses and the water used to determine the temperature of the water in the tank. The model will be used in the future to develop intelligent control algorithms to increase power system efficiency and reliability.

A new maximum power point tracking method for photovoltaic arrays using golden section search algorithm

This paper introduces a new maximum power point tracking (MPPT) method with the golden section search (GSS) algorithm for photovoltaic (PV) systems. The basic principle and the implementation procedures of the GSS algorithm are elaborated in the paper; and the PV simulation model in Matlab/Simulink is also developed. Then the PV system with a boost chopper is modeled and simulated in Simulink. The analytic and simulated results show that the proposed new method has the advantages of fast convergence, noise-resistance, and robustness.

A New Hybrid Anti-Islanding Algorithm in Grid Connected Three-Phase Inverter System

Passive and active methods are two major categories of anti-islanding approaches which are being widely used in grid-connected distributed generation (DG) systems. Passive anti-islanding techniques have no negative impact on the inverters performance however large non-detection zone is the shortcoming of these techniques. On the other hand, active approaches have smaller non-detection zone, but these active approaches inevitably have negative impact on inverters performance. To solve these problems, a new hybrid of both passive and active anti-islanding algorithm is proposed in this paper. A covariance index is used as a passive indicator to activate an active anti-islanding action, adaptive reactive power shift action, which can intelligently vary the output reactive power of the DG system to realize the anti-islanding protections. Both simulation and experimental results have demonstrated that this new algorithm can provide a fast anti-islanding protection while assure the zero or the least perturbation in inverters grid-connected operation.

A centralized fuzzy controller for aggregated control of domestic water heaters

Utilizing aggregated electric loads as system resources has several benefits. It can provide ancillary services for power systems. At the same time, it can provide demand management for electricity customers. It is necessary to develop integrated control strategies for aggregated electric loads. Most domesticwater heaters (DWHs) are electric and they consume much power especially in winter. DWHs represent a substantial share of the residential electricity consumption. Due to the energy storage capability of DWHs they are the best candidates for load control strategies. In this paper, we propose a novel centralized fuzzy controller for peak shaving of the power demand profiles. The proposed centralized controller does not sacrifice customers convenience level. Simulation results show that the proposed centralized control strategy is effective in shaving the aggregated DWHs load demands while filling valleys of the aggregated load profile.

Fuzzy Stochastic Programming Method: Capacitor Planning in Distribution Systems With Wind Generators

Capacitor planning in a distribution system (DS) must account for forecasted load and planned wind generators (WGs). In some DSs with large penetration, WGs support a significant part of the total load on an average and the power flow reverses to the substation during high-wind-low-load conditions. With annual forecasts of peak loads and generations being probabilistic in nature with differing probability distribution functions, their proper representation in a capacitor planning exercise is imperative. In this work, using probabilistic models of load and wind generation, we propose a stochastic capacitor planning formulation for DSs. The proposed formulation minimizes the total cost of newly located and sized capacitors and the annual energy loss in a DS while considering limits on load bus voltages. The starting state of the optimization process has forecasted loads, planned WGs, and inadequate capacitors that results in lower voltage limit violations. In order to handle this infeasible state in a stochastic optimization formulation, fuzzy models are used to represent load bus voltage constraints. The proposed fuzzy stochastic programming method that combines stochastic programming and fuzzy optimization procedure is solved using a robust mixed integer linear programming solver. Results on a 70-bus system are reported, compared, and discussed.

An accurate voltage solution method for radial distribution systems

This paper formulates a set of equations (SOE) to describe a radial distribution system with high R/X ratio. The SOE comprises 3(N - 1) second-order equations and an equal number of variables for an N-bus system. The proposed method solves this SOE using the first-order Newton-Raphson technique. The SOE model accounts for elements such as transformers, transmission lines, shunt capacitors, constant PQ loads, and constant impedance loads. Irrespective of the R/X ratio values of the lines in the system, the SOE preserves a strong diagonal characteristic. As well, this SOE is devoid of bus phase angles, unlike the 2(N - 1) ac power flow equations used to describe transmission systems. The proposed method is compared with other methods reported in the literature for distribution systems, such as Newton-based methods and recursive methodologies that use forward and reverse sweeps. Tests conducted on radial distribution systems with 43, 69, and 5002 buses show the new method to be more accurate than other methods reported in the literature.

PWM Control Strategies for Wind Turbine Inverters

This paper reviews the principles of three PWM control strategies, namely Harmonic Elimination, Current Hysteresis and Space Vector, which are used in the development of a 100kVA IGBT wind turbine inverter. Based on the simulations and the field test results, the comparison and selection issues of these PWM control strategies are investigated and addressed. The simulation and field test waveforms are reproduced.