Riding through Power Disturbances using Local Energy Storage

The nature of Oil and Gas production presents numerous challenges for creating and maintaining reliable electrical infrastructure. Production assets are often widely dispersed over remote geographical areas, leaving them vulnerable to wildlife, storms and poor load management. Unfortunately for ESPs, even an extremely short interruption in electrical service can lead to significant downtime, stress on equipment, and additional field labor costs. The economic and operational advantages to providing a readily available, alternative source of power during these short duration events is significant. This paper describes an effective, economical approach that is based on locally situated energy storage with the appropriate control circuitry. The existing Variable Speed Drive (VSD) architecture allows for the storage system to be constantly connected to the ESP's electrical system, allowing for an ever-present power supply without the need for mechanical switching. The ride-through system was designed to keep a fully loaded ESP system, of corresponding size, operational during most temporary power disturbances. At the conclusion of rigorous laboratory testing, the ride-through system was installed in the field and was subjected to simulated outages. Precision electrical monitoring equipment was installed to record voltage and current during genuine disruptions and interruptions. Records from field testing and genuine events confirmed the viability of local storage systems utilizing supercapacitors. The energy storage system provided a steady supply of power and consistently prevented shutdowns during common power system disruptions. Due to its capacity to store a considerable amount of power, the system demonstrated an ability to ride-through both multiple disturbances in short succession as well as outages lasting as long as 8 seconds, even under full load.

Multivariable State-Feedback Controller Design for PV Inverter Connected to a Weak Grid

This paper presents a robust multi-input multi-output (MIMO) state-feedback control scheme for a photovoltaic (PV) inverter connected to a weak grid. For a weak grid, the point of common coupling (PCC) voltage is very sensitive to the power disturbances and it is dynamically coupled to the PLL dynamics. So far, most of the control methods do not take into accounts these couplings. Therefore, in this paper, the MIMO controller was designed taking into account the dynamics of the phase-locked loop (PLL) and coupling effects between PCC voltage and the active power to enhance the system’s robustness. As result, the robust performance of the PV inverter interfaced to a weak grid was yielded. In addition, the sensitivity of the system to PLL was eliminated, allowing the use of larger PLL bandwidth even in a very weak grid. Based on the eigenvalues analysis method, a comparative study between the proposed control method and the conventional vector control method was performed. The proposed method is verified with simulations in PLECS and real-time simulations in the RT Box. The results show that the proposed MIMO control method preserves the system stability robustness against any change of grid strength, generated power and PLL bandwidth.

A rare-event study of frequency regulation and contingency services from grid-scale batteries

We perform a rare-event study on a simulated power system in which grid-scale batteries provide both regulation and emergency frequency control ancillary services. Using a model of random power disturbances at each bus, we employ the skipping sampler, a Markov Chain Monte Carlo algorithm for rare-event sampling, to build conditional distributions of the power disturbances leading to two kinds of instability: frequency excursions outside the normal operating band, and load shedding. Potential saturation in the benefits, and competition between the two services, are explored as the battery maximum power output increases. This article is part of the theme issue ‘The mathematics of energy systems’.

LabVIEW as Power Disturbances Classification Tools

Power disturbances monitoring is one of the important aspects on dealing power quality issue in electrical system. The aims of conducting monitoring process are to identify the real culprit which contribute to the Power Quality (PQ) problem. One of the vital steps during monitoring process is the classifying various type of power disturbance. This classification process is very important to give a right direction towards proposing the correct mitigation technique. In order to produce reliable classification technique, the devices which has a flexibility on accommodating the software and hardware part need to be deployed. The software is need for algorithm development such as signal processing, Artificial Intelligent (AI) as well as statistical analysis. On the hardware part, the device’s ability to acquire the electrical parameter within the electrical system operation is very important. The data acquisition based on the voltage and current is essential to be feed in the classification algorithm in software side. On the other hand, the interfacing devices and data acquisition module need to be developed at the hardware side, LabVIEW manage to accommodate both software and hardware need and further development of the LabVIEW for this purpose will be elaborated in this chapter.

Novelty Detection Methodology Based on Self-Organizing Maps for Power Quality Monitoring

The inclusion of intelligent systems in the modern industry is demanding the development of the automatic monitoring and continuous analysis of the data related to entire processes, this is a challenge of the industry 4.0 for the energy management. In this regard, this chapter proposes a novelty detection methodology based on Self-Organizing Maps (SOM) for Power Quality Monitoring. The contribution and originality of this proposed method consider the characterization of synthetic electric power signals by estimating a meaningful set of statistical time-domain based features. Subsequently, the modeling of the data distribution through a collaborative SOM’s neuron grid models facilitates the detection of novel events related to the occurrence of power disturbances. The performance of the proposed method is validated by analyzing and assessing four different conditions such as normal, sag, swell, and fluctuations. The obtained results make the proposed method suitable for being implemented in embedded systems for online monitoring.

A New Model of Numerical MHO Distance Relay Associated with Power Swing Detector

In conventional transmission line protection, a distance relay is used to provide the primary as well as backup protection. The voltage and current phasors measurement needed by the distance relay for determining the impedance may be affected by the power disturbances such as power swing. Consequently, this power swing may cause mal-operation of Zone three distance relays which in turn may affect on the reliability of the whole protective scheme. Many power swing blocking functions (PSB) have been developed to mitigate these effects. In this paper, a new model of Mho distance relay with dual-quadrilateral power swing detection characteristic has been developed and implemented first in PC using LabVIEW, then tested using Power System Simulink Model under different faults and power swing conditions. Finally, the relay prototype has been realized using acquisition card NI USB-6009, which acquires real-time signals of the currents and the voltages, processes them digitally and outputs tripping or blocking signal to the circuit breaker. The obtained results show that the relay provides good discrimination between a fault and power swing condition.

Microgrid Frequency Fluctuation Attenuation Using Improved Fuzzy Adaptive Damping-Based VSG Considering Dynamics and Allowable Deviation

Recently, virtual synchronous generators (VSGS) are a hot topic in the area of microgrid control. However, the traditional fixed-parameter-based VSG control methods have an obvious disadvantage. Namely, if the damping value is set to be small, the amplitude of frequency deviations under external power disturbances is large, meaning that the frequency suppression capacity is insufficient, but if the damping value is large, the dynamics of the system will be greatly sacrificed. To solve the problem, taking the dynamic characteristics and the maximum allowable frequency deviation (MAFD) into account, in this paper an improved fuzzy adaptive damping-based VSG control strategy is proposed to simultaneously attenuate the microgrid frequency fluctuations and guarantee the system dynamics. Firstly, in order to address the necessity of using an adaptive damping-based VSG, the structure of a fixed-parameter VSG method that incorporates the f-p/Q-V droop controllers is introduced, based on which a small signal model is established to discuss the impacts of the virtual damping on the frequency response characteristics concerning the different penetration levels of power disturbances. Then, considering the dynamics and MAFD, a fuzzy adaptive controller is constructed relying on the well-designed membership functions, control rules and output scaling factors. The main feature of the improved fuzzy controller is that two alternative output scaling factors are employed to allow the system to be overdamped when the frequency deviation is large and undamped when the frequency deviation is small, balancing the frequency response dynamics and stability characteristics. To verify the effectiveness of the proposed fuzzy adaptive damping-based VSG technique, a computer simulation is conducted on a microgrid system in MATLAB/Simulink, and the obtained results are compared with the conventional droop control and fixed-parameter based VSGs. By using the proposed fuzzy adaptive damping-based VSG control method, the peak frequency deviations under the large power disturbances would become at least 8% lower compared to the traditional droop control and fixed-parameter VSG control, and meanwhile, the frequency response speed is fast when the disturbance stands at a low position. Consequently, it is valuable to promote the proposed techniques in engineering.

Fuzzy Neural Technique Based Dynamic Voltage Restorer for Power Quality Enhancement Under Different Voltage Variations

Abstract— Recently a large interest has been focused on power quality field due to: disturbances caused by non-linear loads, Increase in the number of electronic devices and renewable energy sources. Power quality measures the fitness of the electric power transmitted from generation to the industrial, domestic and commercial consumers. In a power system voltage distortion introduced by harmonics and voltage sags are the most severe affecting power quality, because of both consumers and utilities are affected by these disturbances. Different methods to enhance the power quality but the custom power device is the most effective and efficient method. One of which is the use of Dynamic Voltage Restorer (DVR). The performance of the DVR based on Fuzzy Neural controller to restore the load voltage to its nominal value under different fault conditions and power disturbances is presented in this paper. Simulation results are carried out using MATLAB/SIMULINK program. The faults and disturbances are initiated at 0.8s and kept till 0.95s.