Change implementers’ resistance : considering power and resistance in IT implementation projects

Resistance is normally characterized as a set of behaviours located in and belonging to change recipients. Such behaviours are seen to thwart the legitimate aims of both change strategists and the change agents who implement systems and the associated organisational change on the strategists' behalf. However, results from our case study research indicate that resistance can be a property not only of change recipients’ behaviour, but also of change agents and change strategists. The resistance behaviours identified included the failure to follow a prescribed corporate method and template, a refusal to help or listen, a refusal to fix known problems, the display of an adversarial, confrontational, and/or condescending attitude, subversiveness, a poor work ethic, and a refusal to meet requests. This paper argues for a revised conceptualization of resistance as a behaviour that can be demonstrated by any IT project stakeholders, that cannot be divorced from considerations of power in the IT project context.

Prediction intervals for short-term wind farm power generation forecasts

Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.

Short-term load and wind power forecasting using neural network-based prediction intervals

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Voltage oscillations in power distribution networks in the presence of DFIGs and induction motor loads

This paper investigates the oscillatory behavior of power distribution systems in the presence of distributed generation. The analysis is carried out over a distribution test system with two doubly fed induction type wind generators and different types of induction motor loads. The system is linearized by the perturbation method. Eigenvalues are calculated to see the modal interaction within the system. The study indicates that interactions between closely placed converter controllers and induction motor loads significantly influence the damping of the oscillatory modes of the system. The critical modes have a frequency of oscillation between the electromechanical and subsynchronous oscillations of power systems. Time-domain simulations are carried out to verify the validity of the modal analysis and to provide a physical feel for the types of oscillations that occur in distribution systems. Finally, significant parameters of the system that affect the damping and frequency of the oscillation are identified.

Characteristics and applications of energy storage system to power network - A review

Maintaining reliability and stability of a power systems in transmission and distribution level becomes a big challenge in present scenario. Grid operators are always responsible to maintain equilibrium between available power generation and demand of end users. Maintaining grid balance is a bigger issue, in case of any unexpected generation shortage or grid disturbance or integration of any renewable energy sources like wind and solar power in the energy mix. In order to compensate such imbalance and to facilitate more renewable energy sources with the grid, energy storage system (ESS) started to be playing an important role with the advancement of the state of the art technology. ESS can also help to get reduction in greenhouse gas (GHG) emission by means of integrating more renewable energy sources to the grid. There are various types of Energy Storage (ES) technologies which are being used in power systems network from large scale (above 50MW) to small scale (up to 100KW). Based on the characteristics, each storage technology has their own merits and demerits. This paper carried out extensive review study and verifies merits and demerits of each storage technology and identifies the suitable technology for the future. This paper also has conducted feasibility study with the aid of E-SelectTM tool for various ES technologies in applications point of view at different grid locations. This review study helps to evaluate feasible ES technology for a particular electrical application and also helps to develop smart hybrid storage system for grid applications in efficient way.

Prospects of renewable energy in semi-arid region

Continuous usage of fossil fuels and other conventional resources to meet the growing demand has resulted in in-creased energy crisis and greenhouse gas emissions. Hence, it is essential to use renewable energy sources for more reliable, effective, sustainable and pollution free transmission and distribution networks. Therefore, to facilitate large-scale integration of renewable energy in particular wind and solar photovoltaic (PV) energy, this paper presents the feasibility analysis for semi-arid climate and finds the most suitable places in North East region of Victoria for re-newable energy generation. For economic and environmental analysis, Hybrid Optimization Model for Electric Re-newables (HOMER) has used to investigate the prospects of wind and solar energy considering the Net Present Cost (NPC), Cost of Energy (COE) and Renewable fraction (RF). Six locations are selected from North East region of Victo-ria and simulations are performed. From the feasibility analysis, it can be concluded that Mount Hotham is one of the most suitable locations for wind energy generation while Wangaratta is the most suitable location for solar energy generation. Mount Hotham is also the best suitable locations in North East region for hybrid power systems i.e., com-bination of both wind and solar energy generation.

A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring

When no prior knowledge is available, clustering is a useful technique for categorizing data into meaningful groups or clusters. In this paper, a modified fuzzy min-max (MFMM) clustering neural network is proposed. Its efficacy for tackling power quality monitoring tasks is demonstrated. A literature review on various clustering techniques is first presented. To evaluate the proposed MFMM model, a performance comparison study using benchmark data sets pertaining to clustering problems is conducted. The results obtained are comparable with those reported in the literature. Then, a real-world case study on power quality monitoring tasks is performed. The results are compared with those from the fuzzy c-means and k-means clustering methods. The experimental outcome positively indicates the potential of MFMM in undertaking data clustering tasks and its applicability to the power systems domain.

A comparative study of different power system stabilizers for dynamic stability analysis

Power system stabilizers (PSSs) are extensively used to ensure the dynamic stability of power systems through the modulation of excitation signals supplied to synchronous generators. This paper presents a comparative study of two different PSSs: STAB1 and IEEEST. The stabilizers are designed for the linearized model of a single machine infinite bus (SMIB) system with different loads. Both time-and frequency-domain simulations are carried out to investigate the performance of these stabilizers. For all PSSs, the time-domain simulations are performed by applying a three-phase short-circuit fault at the terminal of the synchronous generator. These simulation results are compared against the open-loop characteristics of the SMIB system where no PSS is implemented. Simulation results demonstrate that the speed-fed PSS provides more damping as compared to frequency- and power-fed stabilizers.

A new fuzzy-based combined prediction interval for wind power forecasting

This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.