This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

This paper presents a new method to enhance the participation of variable-speed wind energy conversion systems (WECS) in existing frequency regulation mechanisms. The proposed approach, based on a modified inertial control scheme, takes advantage of the fast response capability associated with electronically-controlled WECS, allowing the kinetic energy stored by rotational masses to be partly and transiently released in order to provide earlier frequency support. An additional improvement is achieved by communicating the WECS response to conventional generators so that these can eventually take care of the full load imbalance. Several simulations using a two-area test system are performed to demonstrate the benefits of the proposed scheme.

Non-technical electricity losses due to anomalies or frauds are accountable for important revenue losses in power utilities. Recent advances have been made in this area, fostered by the roll-out of smart meters. In this paper, we propose a methodology for non-technical loss detection using supervised learning. The methodology has been developed and tested on real smart meter data of all the industrial and commercial customers of Endesa. This methodology uses all the information the smart meters record (energy consumption, alarms and electrical magnitudes) to obtain an in-depth analysis of the customer’s consumption behavior. It also uses auxiliary databases to provide additional information regarding the geographical location and technological characteristics of each smart meter. The model has been trained, validated and tested on the results of approximately 57000 on-field inspections. It is currently in use in a non-technical loss detection campaign for big customers. Several state-of-the-art classifiers have been tested. The results show that extreme gradient boosted trees outperform the rest of the classifiers.

The main objective of this paper is to describe a multilevel framework that facilitates seamless integration of existing state estimators (SEs) that are designed to function at different levels of modeling hierarchy in order to accomplish very large-scale monitoring of interconnected power systems. This has been a major challenge for decades as power systems grew pretty much independently in different areas, which had to operate in an interconnected and synchronized fashion. The paper initially provides a brief historical perspective which also explains the existing state estimation paradigm. This is followed by a review of the recent technological and regulatory drivers that are responsible for the new developments in the energy management functions. The paper then shows that a common theoretical framework can be used to implement a hierarchical scheme by which even very large-scale power systems can be efficiently and accurately monitored. This is illustrated for substation level, transmission system level as well as for a level between different transmission system operators in a given power system. Finally, the paper describes the use and benefits of phasor measurements when incorporated at these different levels of the proposed infrastructure. Numerical examples are included to illustrate performance of the proposed multilevel schemes.

Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.

Non-technical losses in electricity utilities are responsible for major revenue losses. In this paper, we propose a novel end-to-end solution to self-learn the features for detecting anomalies and frauds in smart meters using a hybrid deep neural network. The network is fed with simple raw data, removing the need of handcrafted feature engineering. The proposed architecture consists of a long short-term memory network and a multi-layer perceptrons network. The first network analyses the raw daily energy consumption history whilst the second one integrates non-sequential data such as its contracted power or geographical information. The results show that the hybrid neural network significantly outperforms state-of-the-art classifiers as well as previous deep learning models used in non-technical losses detection. The model has been trained and tested with real smart meter data of Endesa, the largest electricity utility in Spain.