Measurement-based Method for Wind Farm Power System Oscillation Monitoring

The global increase in the penetration of renewable energy is pushing electrical power systems into uncharted territory, especially in terms of transient and dynamic stability. In particular, the greater penetration of wind generation in European power networks is, at times, displacing a significant capacity of conventional synchronous generation with fixed-speed induction generation and now more commonly, doubly-fed induction generators. The impact of such changes in the generation mix requires careful monitoring to assess the impact on transient and dynamic stability. This paper presents a measurement based method for the early detection of power system oscillations, with attention to mode damping, in order to raise alarms and develop strategies to actively improve power system dynamic stability and security. A method is developed based on wavelet transform and support vector data description (SVDD) to detect oscillation modes in wind farm output power, which may excite dynamic instabilities in the wider system. The wavelet transform is used as a filter to identify oscillations in different frequency bands, while SVDD is used to extract dominant features from different scales and generate an assessment boundary according to the extracted features. Poorly damped oscillations of a large magnitude or that are resonant can be alarmed to the system operator, to reduce the risk of system instability. Method evaluation is exemplified used real data from a chosen wind farm.

Impact of Wind Generation Mix on Transient Stability for an Interconnected Power System

The rapid growth of wind generation in many European countries is pushing power systems into
uncharted territory. As additional wind generators are installed, the changing generation mix may
impact on power system stability. This paper adopts the New England 39 bus system as a test
system for transient stability analysis. Thermal generator models are based on a likely future plant
mix for existing systems, while varying capacities of fixed-speed induction generators (FSIG) and
doubly-fed induction generators (DFIG) are considered. The main emphasis here has been placed
on the impact of wind technology mix on inter-area oscillations following transient grid
disturbances. In addition, both rotor angle stability and transient voltage stability are examined, and
results are compared with current grid code requirements and standards. Results have shown that
FSIGs can reduce tie-line oscillations and improve damping following a transient disturbance, but
they also cause voltage stability and rotor angle stability problems at high wind penetrations. In
contrast, DFIGs can improve both voltage and rotor angle stability, but their power output
noticeably oscillates during disturbances.

Transient Stability Analysis of a Power System with High Wind Penetration

This paper presents transient stability analysis for a power system with high wind penetration. The transient stability has been evaluated based on two stability criteria: rotor angle stability and voltage stability. A modified IEEE-14 bus system has been used as the main study network and simulations have been conducted at several wind power penetration levels, defined as a fraction of total system generation. A wide range of scenarios have been presented based on the wind farm voltage at the point of connection, i.e. low voltage (LV) distribution level and high voltage (HV) transmission level, and the type of wind generator technology, i.e. fixed speed induction generator (FSIG) and doubly-fed induction generator (DFIG).

The Impact of Wind Farm Power Oscillations on the Irish Power System

The impact of power fluctuations arising from fixed-speed wind turbines on the magnitude and frequency of inter-area oscillations has been investigated. The authors introduced data acquisition equipment to record the power flow on the interconnector between the Northern Ireland and Republic of Ireland systems. Through monitoring the interconnector oscillation using a fast Fourier transform, it was possible to determine the magnitude and frequency of the inter-area oscillation between the two systems. The impact of tower shadow on the output power from a wind farm was analysed using data recorded on site. A case study investigates the effect on the system of the removal of a large fixed-speed wind farm. Conclusions are drawn on the impact that conventional generation and the output from fixed-speed wind farms have on the stability of the Irish power system.

Data Reduction of Power System Disturbances using Wavelet Transforms for Network Operation and Control

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the reduction properties of the wavelet transform using real power system data and discusses the application of the reduction method for information transfer in network communications.

Inter-Area Power Oscillation Frequency Mode with Wind Turbine Generator in Irish Power System using PMU Data

Two case studies are presented in this paper to demonstrate the impact of different power system operation conditions on the power oscillation frequency modes in the Irish power system. A simplified 2 area equivalent of the Irish power system has been used in this paper, where area 1 represents the Republic of Ireland power system and area 2 represents the Northern Ireland power system.

The potential power oscillation frequency modes on the interconnector during different operation conditions have been analysed in this paper. The main objective of this paper is to analyse the influence of different operation conditions involving wind turbine generator (WTG) penetration on power oscillation frequency modes using phasor measurement unit (PMU) data.

Fast Fourier transform (FFT) analysis was performed to identify the frequency oscillation mode while correlation coefficient analysis was used to determine the source of the frequency oscillation. The results show that WTG, particularly fixed speed induction generation (FSIG), gives significant contribution to inter-area power oscillation frequency modes during high WTG operation.

Small Signal Stability Performance of Power System during High Penetration of Wind Generation

The small signal stability of interconnected power systems is one of the important aspects that need to be investigated since the oscillations caused by this kind of instability have caused many incidents. With the increasing penetration of wind power in the power system, particularly doubly fed induction generator (DFIG), the impact on the power system small signal stability performance should be fully investigated. Because the DFIG wind turbine integration is through a fast action converter and associated control, it does not inherently participate in the electromechanical small signal oscillation. However, it influences the small signal stability by impacting active power flow paths in the network and replacing synchronous generators that have power system stabilizer (PSS). In this paper, the IEEE 39 bus test system has been used in the analysis. Furthermore, four study cases and several operation scenarios have been conducted and analysed. The selective eigenvalue Arnoldi/lanczos's method is used to obtain the system eigenvalue in the range of frequency from 0.2 Hz to 2 Hz which is related to electromechanical oscillations. Results show that the integration of DFIG wind turbines in a system during several study cases and operation scenarios give different influence on small signal stability performance.

Managing the variability of supply in the European power system with high wind energy penetration

The power system of the future will have a hierarchical structure created by layers of system control from via regional high-voltage transmission through to medium and low-voltage distribution. Each level will have generation sources such as large-scale offshore wind, wave, solar thermal, nuclear directly connected to this Supergrid and high levels of embedded generation, connected to the medium-voltage distribution system. It is expected that the fuel portfolio will be dominated by offshore wind in Northern Europe and PV in Southern Europe. The strategies required to manage the coordination of supply-side variability with demand-side variability will include large scale interconnection, demand side management, load aggregation and storage in the concept of the Supergrid combined with the Smart Grid. The design challenge associated with this will not only include control topology, data acquisition, analysis and communications technologies, but also the selection of fuel portfolio at a macro level. This paper quantifies the amount of demand side management, storage and so-called ‘back-up generation’ needed to support an 80% renewable energy portfolio in Europe by 2050.

Power system performance of offshore wind in the United Kingdom in 2030

At least 34 % of the United Kingdom’s power must come from renewable energy sources to meet planned European Union targets in 2030. Wind power will provide the majority of this renewable electricity with an estimated 36 GW offshore and 21 GW onshore. The success of the Crown Estate’s leasing rounds 1 and 2 in offshore wind has meant the United Kingdom is now one of the world leaders in offshore wind power development. Leasing round 3 will see offshore wind in the United Kingdom surpass 36 GW of installed capacity. This is a significant increase from the current installed offshore wind capacity of 3.6 GW. This research investigates the power system performance of offshore wind power in the United Kingdom in 2030.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.