The integration of Artificial Intelligence in Relay protection has brought numerous possibilities for optimizing the performance of electrical network protection systems. Over the last decade, the use of smart algorithms and machine learning has created a transformation in the power and energy industry. By leveraging these techniques, power systems engineers can better understand the operating conditions of power systems and optimize the performance of relay protection. 

One example of AI integration is the use of pattern recognition to detect network anomalies. Power system anomalies arise from issues such as equipment faults, weather disturbances, and human error. These anomalies have a significant impact on the performance of relay protection and compromise the system's reliability. With AI, power systems engineers can now detect system anomalies from data collected from power system sensors and waveforms. These anomalies can then be used in designing better algorithms for enhanced protection.

Another example of AI integration is the development of Machine learning algorithms for predicting power system faults. With Machine learning algorithms, power system operators can predict the likelihood of fault occurrences in real-time and enable corrective measures before they cause significant harm. 

For instance, Artificial Intelligence can be leveraged in the development of digital twin systems that mimics the power system in real-time. These digital twin systems can use machine learning algorithms to simulate various relay protection system performance scenarios. By simulating scenarios such as relay faults, a strategic response can be deployed to enhance the system's overall performance. 

However, the integration of artificial intelligence in relay testing is not devoid of challenges. One significant challenge is the development of robust AI models that can accurately predict real-world operating conditions. Often, Machine learning models rely on well-defined algorithms that cannot account for the uncertainty associated with power system operations.

Another challenge is the cost implications of maintaining AI-integrated relay protection systems. As with all technological innovations, the cost of maintaining AI systems can be significantly high. Training personnel to operate and manage complex AI systems come with additional costs, and for smaller utilities, this can be a significant deterrent against integrating AI in their relay protection systems. 

In conclusion, the integration of Artificial intelligence in relay protection systems has brought a new level of sophistication in electrical network protection systems. The possibilities of AI in network protection are endless, with the potential to improve system robustness, and enhance reliability. The challenges notwithstanding, the continued innovative research and development of AI systems will revolutionize relay protection and ensure that electrical systems are better protected.