Glenn Platt

Integrated Distribution Systems Planning to Improve Reliability Under Load Growth

In this paper, an integrated methodology is proposed for planning distribution networks in which the operation of distributed generators (DGs) and cross-connections (CCs) is optimally planned. Distribution lines and high-voltage/medium-voltage (HV/MV) transformers are also optimally upgraded in order to improve system reliability and to minimize line losses under load growth. An objective function is constituted, composed of the investment cost, loss cost, and reliability cost. The energy savings that result from installing DGs is also included in this function. The bus voltage and line current are maintained within their standard bounds as constraints. DG output power is used as another constraint, where this should not be less than 30% of the generators rated power; otherwise, the unit is not switched on. The modified discrete particle swarm optimization (PSO) method is employed in this paper for optimizing this planning problem. To evaluate the proposed approach, the distribution system connected to bus 4 of the Roy Billinton test system is used. Four different scenarios are assessed. In the first scenario, a basic planning approach is studied. In the second scenario, the use of DG is planned to avoid the line and transformer upgrading. In the third scenario, CC-based planning is studied when no DG exists. Finally, the proposed technique, in which all technologies are included, is investigated in the fourth scenario. The outcomes demonstrate that the lowest cost plan results when all technologies are incorporated as proposed in this paper. A study is performed to evaluate the accuracy and robustness of the proposed PSO-based optimization method. The results highlight the applicability of this method for solving the distribution network planning problem.

Predictive Direct Virtual Torque and Power Control of Doubly Fed Induction Generators for Fast and Smooth Grid Synchronization and Flexible Power Regulation

Predictive direct torque control of the electric motors has been well developed. It is simple and has excellent steady state and transient performance. However, further developments are still under investigation for applications in the field of power generation. This paper presents a predictive direct virtual torque and power control strategy for a doubly fed induction generator, which allows fast and smooth grid synchronization, and flexible active and reactive power regulation. In the no-load mode, predictive direct virtual torque control is employed to meet the grid synchronization conditions. In the grid-connected mode, predictive direct power control is utilized to achieve flexible active and reactive power regulation. To simplify the control system structure and improve the reliability, a sensorless rotor position scheme is proposed. Furthermore, a model-based predictive scheme is introduced to compensate for a one-step delay in the digital implementation. The proposed control strategy is very simple and robust. There is constant switching frequency, while the requirement of smooth and fast grid synchronization is fulfilled. The transition from no load to flexible power regulation is achieved without changing the switching table. The proposed control strategy was tested by simulation using MATLAB/Simulink and experimentally validated on a 20-kW laboratory prototype.

A deployed multi-agent framework for distributed energy applications

In this paper, we describe the adaptation of an open-source multi-agent platform for distributed energy applications and the trial deployment of resource-controller agents. The platform provides real-time, two-way communication and decision making between distributed energy resources --- loads and generators --- in electricity distribution networks. Such a decentralized architecture improves grid reliability, allows consumers to play a more active role in their energy usage, benefits the network by alleviating the effects of peak wholesale prices and network constraints, and creates new business opportunities in a deregulated market. Agents have been deployed to control hardware at trial locations in Australia, providing a realistic test environment for the platform, and medium-scale trials are anticipated in the near future.

Multi-Objective Model-Predictive Control for High-Power Converters

This paper presents a multi-objective model-predictive control (MOMPC) strategy for controlling converters in high-power applications. The controller uses the system model to predict the system behavior in each sampling interval for each voltage vector, and the most appropriate vector is then chosen according to an optimization criterion. By changing the cost function properly, multiobjectives can be achieved. To eliminate the influences of one step delay in digital implementation, a model-based prediction scheme is introduced. For high-power applications, the converter switching frequency is normally kept low in order to reduce the switching losses; this is done by adding a nonlinear constraint in the cost function. However, to avoid system stability deterioration caused by the low switching frequency, an N-step horizontal prediction is proposed. Finally, the control algorithm is simplified using a graphical algorithm to reduce the computational burden. The proposed MOMPC strategy was verified numerically by using MATLAB/Simulink, and validated experimentally using a laboratory ac/dc converter.

Powering Through the Storm: Microgrids Operation for More Efficient Disaster Recovery

Disasters, whether natural or man-made, compromise the quality of life for all involved. In such situations, expeditious recovery activities are deemed imperative and irreplaceable for the restoration of normalcy. However, recovery activities rely heavily on the critical infrastructures that supply basic needs like electricity, water, information, and transportation. When disasters strike, it is likely that the critical infrastructures themselves are affected significantly, hampering efficient recovery processes, thus presenting a Catch-22 conundrum. In this article, we present examples from different parts of the world where distributed energy resources, organized in a microgrid, were used to provide reliable electricity supply in the wake of disasters, allowing recovery and rebuilding efforts to occur with relatively greater efficiency.

Smart grid — The next generation electricity grid with power flow optimization and high power quality

As the demand for electricity power increases rapidly while the fossil fuels decrease year by year, taking use of renewable resources seems very necessary. However, due to the discontinuous nature of the renewable resources and the hierarchical topology of the existing grid, the power quality and grid stability will deteriorate as more and more distributed generations (DGs) connected to the grid. It is a good idea to combine local utilization, local consumption, energy storage and DGs to form grid-friendly micro grid, these micro grids are then assembled to be an intelligent power system ─ Smart Grid. It can optimize the power flow and integrate power generation and consumption effectively. Most importantly, the power quality and grid stability can be improved greatly. This paper depicts how smart grid addresses the current issues of power system. It also figures out the key technologies and expectation of the smart grid.

Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems

Graphical abstractDisplay Omitted HighlightsOur algorithm aims to improve the feature quality in general fault diagnosis system.The algorithm filters out redundant features using consensus evolutionary clustering.The algorithm was tested on the ASHRAE-1312-RP experimental fault data.Sensitivity & specificity were >95%, with considerably less false positives up to as low as 1.6%. Various sensory and control signals in a Heating Ventilation and Air Conditioning (HVAC) system are closely interrelated which give rise to severe redundancies between original signals. These redundancies may cripple the generalization capability of an automatic fault detection and diagnosis (AFDD) algorithm. This paper proposes an unsupervised feature selection approach and its application to AFDD in a HVAC system. Using Ensemble Rapid Centroid Estimation (ERCE), the important features are automatically selected from original measurements based on the relative entropy between the low- and high-frequency features. The materials used is the experimental HVAC fault data from the ASHRAE-1312-RP datasets containing a total of 49 days of various types of faults and corresponding severity. The features selected using ERCE (Median normalized mutual information (NMI)=0.019) achieved the least redundancies compared to those selected using manual selection (Median NMI=0.0199) Complete Linkage (Median NMI=0.1305), Evidence Accumulation K-means (Median NMI=0.04) and Weighted Evidence Accumulation K-means (Median NMI=0.048). The effectiveness of the feature selection method is further investigated using two well-established time-sequence classification algorithms: (a) Nonlinear Auto-Regressive Neural Network with eXogenous inputs and distributed time delays (NARX-TDNN); and (b) Hidden Markov Models (HMM); where weighted average sensitivity and specificity of: (a) higher than 99% and 96% for NARX-TDNN; and (b) higher than 98% and 86% for HMM is observed. The proposed feature selection algorithm could potentially be applied to other model-based systems to improve the fault detection performance.

A droop control strategy of parallel-inverter-based microgrid

In this paper, the control strategy for a parallel-inverters-based microgrid is presented. The control strategy includes outer active and reactive power control loop based on frequency and voltage droop method to avoid critical communications among distributed generation units (DGs). The inner inverter control loop is employed to produce the required inverter output voltage provided by the outer power loop. In addition, two inverter control schemes are introduced and compared. This microgrid can operate at both grid-connected and islanded mode with proper power sharing capability between parallel DG systems. Moreover, smooth transition between operating modes is achieved without causing negative effect on the utility and critical loads. The performance of this control strategy is verified in simulation using Matlab/Simulink.

Distributed wireless sensor networks and industrial control systems - a new partnership

CSIRO is investigating the application of mote devices as intelligent agents in a distributed energy control system. This is a significant paradigm shift from the application of motes as sensor networks - it involves the interaction of motes and industrial control systems hardware, and this paper details our early development work in this area.

Optimal supervisory HVAC control: Experiences in Australia

A number of HVAC system vendors and operators have started to implement demand management systems in commercial buildings, yet this study contends that the impact of such systems on building performance and occupant comfort is poorly understood. This article examines such issues, showing the results from demand response experiments in two large office buildings. For optimal performance, it is believed that the HVAC management system should be dynamic and intelligent, responding to changing events and considering a variety of external factors, such as occupancy, human comfort, electricity price, and weather forecast. An example of such a system is detailed.