M.A.M.M. van der Meijden

Neural Network-based Load Forecasting and Error Implication for Short-term Horizon

Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often hindered due to various load impacting factors. Two of the major impacting factors are day-ahead weather forecast and subsequent variation in electricity demand that is independent of weather. To tackle the uncertainty in short-term load forecasting, this paper presents a neural network-based load forecasting technique for short-term horizon based on data corresponding to a U.S. independent system operator. With the real life data, a better understanding of forecasting error is carried out while further identifying the time periods when the load is supposedly to be over- or under-forecast.

Classification, domains and risk assessment in asset management: A literature study

This paper presents a literature study on asset management in electrical power transmission and distribution system. Due to restructure and deregulation of electric power industry in recent times, the focus has been on transmission and distribution assets that include transmission lines, transformers, power plants, substations and support structures. The study aims to provide a first of its kind exposure to asset management classification, various interesting maintenance methods and theories developed in last two decades. In the end, it also discusses various risk assessment techniques in asset management developed and used for academic research and industries.

Implementation of Slow Coherency Based Controlled Islanding Using DIgSILENT PowerFactory and MATLAB

Intentional controlled islanding is a novel emergency control technique to mitigate wide-area instabilities by intelligently separating the power network into a set of self-sustainable islands. During the last decades, it has gained an increased attention due to the recent severe blackouts all over the world. Moreover, the increasing uncertainties in power system operation and planning put more requirements on the performance of the emergency control and stimulate the development of advanced System Integrity Protection Schemes (SIPS). As compared to the traditional SIPS, such as out-of-step protection, ICI is an adaptive online emergency control algorithm that aims to consider multiple objectives when separating the network. This chapter illustrates a basic ICI algorithm implemented in PowerFactory. It utilises the slow coherency theory and constrained graph partitioning in order to promote transient stability and create islands with a reasonable power balance. The algorithm is also capable to exclude specified network branches from the search space. The implementation is based on the coupling of Python and MATLAB program codes. It relies on the PowerFactory support of the Python scripting language (introduced in version 15.1) and the MATLAB Engine for Python (introduced in release 8.4). The chapter also provides a case study to illustrate the application of the presented ICI algorithm for wide-area instability mitigation in the PST 16 benchmark system.

Optimal management of reactive power sources in far-offshore wind power plants

This paper introduces a new approach for the optimal management of reactive power, with emphasis on offshore wind power plants. The approach follows a predictive optimization scheme (i.e. day-ahead, intraday application). Predictive optimization is based on the principle of minimizing the real power losses, as well the number of On-load Tap Changer (OLTC) operations for daily time horizon (discretized in 24 hours). The mixed-integer nature of the problem and the restricted computing budget is tackled by using an emerging metaheuristic algorithm called Mean-Variance Mapping Optimization (MVMO). The evolutionary mechanism of MVMO is enhanced by introducing a new mapping function, which improves its global search capability. The effectiveness of MVMO to find solutions that ensure minimum losses, minimum impact on OLTC lifetime, and well as optimal grid code compliance is demonstrated by investigating the case of a real world far-offshore wind power plant with HVDC connection.

A post-processing methodology for robust spectral embedded clustering of power networks

Partitioning of electric networks into zones or areas is a procedure that has numerous applications in power system planning, operation and control. Spectral clustering based approaches are among the most favoured ones to solve the partitioning problem. Applications of spectral clustering include definition of control zones, analysis of connectivity structure of power networks, intentional controlled islanding, design of sectionalising strategies, and visualisation. Although spectral clustering is a state-of-the-art family of methods with numerous extensions, some practical issues can arise when applying it to large-scale power networks. While spectral clustering becomes significantly more robust to outliers when combined with a robust post-processing method like k-medoids, the connectedness of the resulting partitioning cannot be guaranteed. This paper proposes a greedy algorithm to solve the connectedness issues inherent to many robust post-processing methods. Furthermore, it is proposed to utilise a label propagation based heuristic to improve the quality of the final partitions. The test results evaluate the steps of the methodology on a large-scale 1354-bus PEGASE test network.

Volatility in electrical load forecasting for long-term horizon — An ARIMA-GARCH approach

Electrical load forecasting in long-term horizon of power systems plays an important role for system planning and development. Load forecast in long-term horizon is represented as time-series. Thus, it is important to check the effect of volatility in the forecasted load time-series. In short, volatility in long-term horizon affects four main actions: risk management, long-term actions, reliability, and bets on future volatility. To check the effect of volatility in load series, this paper presents a univariate time series-based load forecasting technique for long-term horizon based on data corresponding to a U.S. independent system operator. The study employs ARIMA technique to forecast electrical load, and also the analyzes the ARCH and GARCH effects on the residual time-series.

Effects of grounding configurations on post-contingency performance of MTDC system: A 3-Terminal example

The grounding system is extremely important, as it affects the performance of the MTDC system virtually in any possible mode: normal (asymmetrical operation) and abnormal operation (faults), steady-state and dynamic. The objective of this paper is to introduce a simple approach to assess the steady-state post-contingency of multi-Terminal HVDC System and uses it order to illustrate the effects of grounding configurations on steady-state post-contingency performance. A 3-terminal HVDC system is used to formulate the main theoretical framework for performance prediction on post-contingency steady-state of MTDC system as well as for demonstrative purposes.