Rabie Belkacemi

Experimental implementation of Multi-Agent System algorithm for distributed restoration of a Smart Grid System

There have been numerous simulation works done on Grid Restoration using Multi-Agent System but almost no experimental work to ascertain the duplicity of these simulation results. This work aims to experimentally perform distributed restoration of a smart power grid system. The concept used in this research is based on the distributed and intelligent multi-agent system technology where multiple smart entities are geographically spread and if equipped with two-way communication capability these entities are able to reach goals or solutions that would have been impossible to reach with non-smart entities. The technology is implemented through the use of a six-bus experimental test bed set up using Tennessee Technological University Smart Grid Laboratory which is undergoing rapid development. The experimental results obtained align with simulation results published earlier and show that the proposed system can restore power in a timely manner without violating any constraints.

Real-Time Cascading Failures Prevention for Multiple Contingencies in Smart Grids Through a Multi-Agent System

Cascading failures (CF) is complex in nature and difficult to accurately model or solve mathematically. The current industry approach to preventing CF, which leads to blackout event, involves incurring losses. In this paper, a technique based on an adaptive multi-agent system algorithm is implemented to prevent CF without loss incurrence. The algorithm uses mathematical combinations heuristically selected through the use of sensitivities obtained from the economic dispatch history of the power system to redispatch the power from the generators. This approach enables the implementation of the algorithm on systems of any size. The algorithm is experimentally applied in real-time with the consideration of necessary constraints as it halts the occurrence of CF. The test system is an experimental set up of the generation and transmission side of the IEEE 30-bus system using a reconfigurable smart grid laboratory hardware developed for testing algorithms requiring two-way communication capabilities. It was first showed that the test system will experience CF if nothing is done to prevent CF after the occurrence of a contingency. A detailed experimental analysis of the ensuing blackout event is given. The algorithm was used to prevent CF in the system after the occurrence of N-1 and N-1-1 contingencies. The algorithm was also tested on a larger system, the IEEE 118-bus system, through simulation. The experimental and simulation results affirm the efficacy of the proposed algorithm for systems of any size. In fact, it was discovered from our results that the large number of generators in large systems helps the algorithm converge faster than it does for small systems, which have restricted resources and combinations.

Multi-Agent System algorithm for preventing cascading failures in smart grid systems

In this work, a technique based on an adaptive Multi-Agent System algorithm is implemented to solve the complex problem of cascading failure events which lead to total blackout. This method proposes a solution to a variant of cascading failure events and is unique as previous literature focuses on identifying the possibility of occurrence of the cascading failures and then mitigates the failures. The proposed solution which utilizes pre-stated mathematical combinations that aim to redispatch the power from the generators is dynamically and experimentally applied in real-time, therefore it considers all the active factors and constraints involved as it halts the occurrence of cascading failures after an N-1 contingency. The distributed and intelligent algorithm is modeled to suit power system applications and then implemented on an experimental set up of the generation and transmission side of the IEEE 30-bus system utilizing a reconfigurable Smart Grid Laboratory hardware developed for testing distributed algorithms requiring two way communication capabilities.

Restoration of smart grid distribution system using two-way communication capability

In this Work, we investigate the use of two-way communication to perform distributed restoration of smart power grid distribution systems. The concept used in this research is based on the distributed and intelligent multi-agent system technology where multiple smart entities are geographically spread and if equipped with communication capability these entities are able to reach goals or solutions that would have been impossible to reach with a single or a local control. The technology is implemented on the West Virginia Super Circuit to validate the theory. The results show that proposed system can restore the power in a timely manner without violating any constraints.

Intelligent mitigation of blackout in real-time microgrids: Neural Network Approach

In this paper, a novel application of Artificial Neural Networks (ANN) is deployed to prevent blackout in a microgrid after N-1-1 contingency condition. In fact, microgrids are vulnerable to disturbances and abnormal conditions due to their inherent small inertia. Therefore, stability of microgrids after a disturbance turns into a challenge in power systems. The key contribution of this paper is to utilize the artificial intelligence concept to prevent cascading failure practically at early stages and to make microgrids more reliable and robust by intelligent and adaptive re-dispatch of power. The proposed ANN control approach is tested on an experimental testbed microgrid. Experimental results verify the robustness, accuracy, and effectiveness of the ANN method for preventing cascading failure in addition to providing voltage and frequency stability after initiation of a disturbance.

Experimental implementation of Multi-Agent System Algorithm to prevent Cascading Failure after N-1-1 contingency in smart grid systems

Cascading Failure and Blackout Event is one of the major concerns to all in the Power System industry. Recent literature mainly focused on predicting the occurrence of this catastrophic event and suggestions on ways to curtail the spread of Cascading Failure. No research, to our knowledge, as focused on preventing Cascading Failure without the use of high cost precautionary steps. This work utilizes an adaptive Multi-Agent System Algorithm in a smart grid system with two-way communication capability to successfully prevent Cascading Failure and Blackout Event after N-1-1 transmission line contingency condition with more than one transmission line overloaded. The proposed algorithm stops the cascading failure after the N-1-1 contingency by redispatching the power from the generators through the use of pre-stated mathematical combinations and the consideration of necessary constraints and factors. We obtained experimental results instead of the popular simulation results that has been the norm for publications on Cascading Failures and Blackout Event. The generation and transmission side of IEEE 30-bus system was used as the experimental test bed. It was ascertained that the test bed can experience a Cascading Failure and Blackout Event if no preventive measure was taken. We then utilized the algorithm to prevent Cascading Failure. The experimental results affirm the efficacy of the proposed algorithm.

Experimental Transient Stability Analysis of MicroGrid systems: Lessons learned

A Transient Stability Analysis of a low inertia microgrid system is presented in this paper. The analysis is performed on experimental data collected for different case scenarios of grid disturbances. The test bed consists of a reconfigurable 100kW MicroGrid system with more than 10 rotating generators, PV, and Wind penetration capabilities. Both islanded and non-islanded situations are considered for a solid three phase fault analysis and comparison. The paper also presents the results of fault scenario when the rotating machines are pushed to an unstable region. The experimental results and system behavior observed show interesting phenomena. The MicroGrid showed higher resiliency for faults than expected even if the relative angles are more than 160° and even if the fault is sustained for a long time.

Real-time measurement of frequency using affordable rotary encoder and LabVIEW

The accuracy of measurements from a rotary encoder is of high importance and the level of accuracy desired will depend on the nature of the application in which the encoder is used. Due to the various errors in the rotary encoder measurement during real-time usage, this work has designed a LabVIEW program that exempts erroneous readings that are not within the chosen range, forms an array of readings within the acceptable range and finds the mean of the array once the size of the array is equal to the chosen size of the array stipulated in the program. This mean value is given as the actual measurement. This gives a more accurate real-time measurement than is the case without the program. A motor-generator set frequency measurements is used to validate the effectiveness of this program and frequency readings on the inverter driving the motor are compared with that displayed on the system through the program. Some analyses are carried out in order to ascertain the dependency of the accuracy of the measurements acquired on the array size utilized in the program.

Genetic algorithm based optimized load frequency control for storageless photo voltaic generation in a two area multi-agent system

In this paper, a multi-agent load frequency balancing control algorithm based on Genetic Algorithm for a storage-less Photo Voltaic (PV) generation is proposed. For maximum deployment of available renewable energy, the PV generation is tracked at its maximum power point however through the use of a virtual synchronous generator converter control, the PV generator is able to follow the load demand so far its possible maximum power remains in excess of the load demand. When the maximum power falls below demand, the conventional synchronous generator in the second area covers the power deficit. Through Genetic Algorithm based Optimization, the virtual inertia parameter is determined for optimum performance of the PV load following capability. Through the Universal Multi Agent Platform (UMAP), we are able to communicate the frequencies of the two areas (representing two agents) to a central agent which computes the Area Control Error (ACE) and therefore take frequency bias into account in our system model. By implementing load following without any energy storage, we significantly reduce costs accrued from energy storage installations.

Adaptive Immune System reinforcement Learning-Based algorithm for real-time Cascading Failures prevention

Abstract Artificial intelligent algorithms have found a wide-range of applications in power systems, especially in solving long-existing problems immune to non-intelligent algorithms. Cascading Failures (CF), one of such problems, require load shedding as a current industrial solution. Load shedding results in losses to all power system stakeholders. This work proposes the use of an Artificial Immune System (AIS) algorithm to intelligently adjust the power output of the generators in the power system relative to one another in real time to prevent CF. AIS gives the artificial intelligent algorithm reinforcement learning capability by enabling it to pick the appropriate combination(s) for a particular system state; hence, the algorithm is called Immune System Reinforcement Learning-Based (ISRL-Based) algorithm. The algorithm was trained offline using both static and dynamic power equations and the effectiveness of both approaches was evaluated through statistical deviation. Analyses showed that using dynamic equations resulted in a more accurate solution than the static equations. CF was dynamically simulated on the IEEE 118-Bus system after an N-2 contingency, the results obtained agrees with the results from the analysis of the 2003 Northeast USA CF event. The effectiveness of the algorithm and online training were also experimentally validated after an N-1-1 contingency in a nine-bus system.