Genetic Algorithm-Based Optimization of Overcurrent Relay Coordination for Improved Protection of DFIG Operated Wind Farms

Abstract

Rigorous protection of wind power plants is an immensely momentous aspect in electrical power protection engineering which must be contemplated thoroughly during designing the wind plants to afford a proper protection for power components in case of fault occurrence. The most commodious protection apparatus are overcurrent relays (OCRs) which are responsible for protecting power systems from impending faults. These relays are set and coordinated with each other by applying IEEE or IEC standards methods, however, their operation times are relatively long and the coordination between these relays are critical. The other common problem in wind farm protection systems is when a fault occurs in a plant, several OCRs operate instead of a designated relay to that particular fault location. This undesirable action can result in unnecessary power loss and disconnection of healthy feeders out of the plant which is extremely dire. Therefore, this research proposes a novel genetic algorithm (GA) based optimization for proper coordination of OCRs to improve their functions for protection of wind farms. GA optimization technique has some advantages over other intelligent algorithms including high accuracy, fast response and most importantly achieving optimal solutions for nonlinear characteristics of OCRs. In this work the improvement in protection of wind farm is achieved through optimizing the relay settings, reducing their operation time, time setting multiplier of each relay, improving the coordination between relays after implementation of IEC 60255-151:2009 standard. It is found that the new approach achieves significant improvement in operation of OCRs at the wind farm and drastically reduces the accumulative operation time of the relays.

Optimal Placement of Reclosers in a Radial Distribution System for Reliability Improvement

“…After the zone formation, the optimal positioning of reclosers in each zone is done by evaluating the objective function (Equation ( 16)) using the GA optimization technique [27] in the MATLAB environment. The results of the optimized placement of reclosers in the 69-bus system exhibited in Figure 5 zones are arranged in Table 7.…”

Distribution network monitoring and management system based on intelligent recognition and judgement

“…The interaction and coordination of each Agent on the high-/low-voltage side are completed by the task coordination agent, and the task coordination agents can communicate with each other. Each Agent is connected to the management layer through the task decomposition agent, enabling different agents to connect to the corresponding management layer systems [10]. The various systems of the management layer are connected through the communication and interaction between the agents to realise the interconnection, interoperability and mutual coordination of the operating platform.…”

Smart Sensors for Smart Grid Reliability

“…The programming of the relay-based smart-sensor optimization coordination was carried out by means of a GA. Genetic algorithms belong to the meta-heuristic algorithms which have proven to be suitable for global search optimization techniques, dealing with linear and nonlinear, continuous or discontinuous as well as convex problems [47][48][49][50]. GA is a multipoint and population-based search methodology, so that the possibility of finding a global optimum solution is higher than that of other optimization methods, such as single-point search methodology, due to the possibility to explore the search space in different directions simultaneously [51,52].…”