Optimized Design of Intelligent Power Monitoring Systems for Distributed Generation

Against the backdrop of global energy transition, distributed generation is increasingly becoming a vital component of power supply. With continuous advancements in renewable energy technologies, the widespread adoption of distributed energy sources such as solar and wind power has injected new momentum into the realization of a low-carbon economy. This model enhances energy utilization efficiency, reduces transmission losses, and improves the flexibility and reliability of power systems.

According to power system theory, grid reliability and stability heavily depend on the effective management of various generation sources. The complexity of modern power systems demands more precise control and dispatch within distributed generation environments—especially amid growing load fluctuations and resource uncertainty. To address these challenges, intelligent power monitoring systems have emerged, leveraging advanced information and communication technologies to enable real-time monitoring and dynamic adjustment of power resources. This paper explores the design of intelligent power monitoring systems and optimized control in distributed generation, aiming to contribute to energy transition and the achievement of sustainable development goals.

1. Power Monitoring
Power monitoring is a critical approach for real-time supervision, data acquisition, and analysis of power system operations, aiming to ensure the safety, reliability, and efficiency of power systems. A power monitoring system primarily consists of data acquisition units, data transmission networks, monitoring and management platforms, and alarm and response mechanisms. Data acquisition units collect operational data from various power equipment—such as generators, transformers, and distribution devices—including key parameters like voltage, current, frequency, and power factor.

The collected data is then transmitted via stable and secure communication networks (e.g., fiber optics, wireless transmission) to the monitoring center. An efficient data transmission network ensures the timeliness and integrity of information, providing a reliable foundation for subsequent analysis. The monitoring and management platform conducts real-time monitoring and analysis of the acquired data, utilizing technologies such as big data analytics and cloud computing to provide visualized interfaces and decision support, assisting operators in making effective decisions.

2.System Design

2.1 System Architecture

The architecture of the intelligent power monitoring system is shown in Table 1.

In the architecture of the intelligent power monitoring system, the functions of each layer complement their respective key technologies, forming an efficient operational framework. The perception layer acquires real-time data through sensors and smart meters, serving as the foundation and prerequisite for system functionality. The accuracy and timeliness of data directly affect the quality of subsequent analysis.

The network layer acts as a data transmission hub, utilizing advanced technologies such as fiber optics and wireless communication to ensure data is transmitted quickly and reliably to the monitoring center. It must also ensure data integrity and security, preventing loss or tampering during transmission. The application layer is responsible for in-depth data analysis and visualization, leveraging advanced data processing algorithms and big data technologies to transform massive datasets into valuable insights, supporting managers in making precise decisions.

2.2 Hardware Selection

The system hardware components and their main performance parameters are shown in Table 2.

2.3 Data Communication Strategy

2.3.1 Data Collection and Transmission

Data collection and transmission are core components of the intelligent power monitoring system, directly influencing the system's real-time performance and effectiveness. In this process, various sensors and monitoring devices in the perception layer collect key operational data from the power system—such as voltage, current, power, and frequency—as well as operational status information from distributed generation sources.

To ensure data accuracy, the acquisition devices must possess high precision and high reliability [10]. After collection, the data is transmitted to the network layer, primarily using modern communication technologies such as fiber optic communication, wireless communication, and Internet of Things (IoT) technologies. Fiber optic communication, with its high bandwidth and low latency, is suitable for large-scale data transmission scenarios. Wireless communication offers flexibility and convenience, effectively covering various monitoring points through wireless signals.

2.3.2 Security Measures

In intelligent power monitoring systems, security measures such as data encryption, network security protection, and access control form a multi-layered security framework. This framework effectively mitigates external attacks and internal risks, laying a secure foundation for the implementation of intelligent power management. Implementing strong encryption algorithms during data transmission prevents data from being intercepted or tampered with. The use of symmetric encryption algorithms such as the Advanced Encryption Standard (AES) ensures that only users with the correct decryption key can access the data, thereby protecting the integrity and confidentiality of sensitive information and ensuring that data remains unaltered during transmission. Regarding network security protection, the interconnection of multiple devices and systems significantly increases the risk of cyberattacks. Therefore, deploying security devices such as firewalls, Intrusion Detection Systems (IDS), and Intrusion Prevention Systems (IPS) enables real-time monitoring of network traffic, identification, and blocking of suspicious activities, preventing malicious attacks from affecting the system and enhancing overall security. User access control and authentication mechanisms, such as Role-Based Access Control (RBAC), ensure that only authorized users can access specific system functions and data. This reduces the risk of internal data leaks, improves system security, and effectively prevents unauthorized access.

3. Research Methodology

3.1 Research Design

This study adopts a combined approach of experimental and simulation methods, integrating real-world electricity market data with simulated power demand to construct multiple experimental scenarios.

These scenarios enable comprehensive testing and evaluation of the system. In the experimental design, system performance evaluation primarily focuses on metrics such as scheduling efficiency, resource utilization, and response time. By configuring different loads, resource allocations, and generation modes, the system’s performance under various operating conditions is simulated.Security evaluation, on the other hand, focuses on the system's resilience against unexpected events such as cyberattacks, system failures, and data breaches.

To comprehensively assess the performance of the intelligent power monitoring system, a scientific evaluation framework and indicator system were designed, encompassing performance metrics—including response time, scheduling success rate, resource utilization, and system stability—and security metrics—such as intrusion detection rate, vulnerability patching time, and data encryption strength.

3.2 Performance Evaluation

The performance evaluation of the intelligent power monitoring system in optimized control of distributed generation is shown in Table 3.

The security evaluation metrics in Table 4 provide comprehensive protective measures for the intelligent power monitoring system. These metrics cover aspects such as data encryption, intrusion detection, access control, vulnerability remediation, and malware protection, ensuring the system can effectively respond to potential threats including cyberattacks, data breaches, and malicious software.

For example, the data encryption level requires the use of AES-256 or higher encryption standards to ensure the security of data transmission and storage; the intrusion detection rate target is above 95%, ensuring the system can promptly identify and respond to abnormal access or attack behaviors. Access control effectiveness must achieve 100% compliance, ensuring user permission management strictly adheres to security policies. The target for security vulnerability remediation time is within 24 hours, enabling rapid resolution of identified vulnerabilities.

4. Experimental Results

4.1 Performance Test Results

The performance test results are shown in Table 5.

In this performance test, all system metrics performed well, meeting or exceeding the preset target values. The system’s response time was 1.8 s, satisfying the <2.0 s requirement, indicating high scheduling efficiency. The data processing speed reached 2,200 records per second, surpassing the 2,000 records/s requirement, demonstrating strong real-time data processing capability. System availability was 99.98%, higher than the 99.95% target, reflecting excellent stability and reliability. Energy loss rate was 2.5%, below the 3.0% target, optimizing power transmission efficiency. Optimization scheduling success rate reached 92%, effectively supporting the system's dispatch objectives. Fault recovery time and resource utilization were 4 minutes and 87%, respectively—both outperforming the established standards—demonstrating the system’s fast recovery capability under faults and efficient resource utilization. The results indicate that the intelligent power monitoring system exhibits strong overall performance in the optimized control of distributed generation.

4.2 Security Test Results

The security test results are shown in Table 6.

In the security testing, the system demonstrated a high level of protection, with all security metrics meeting or exceeding the expected targets. The data encryption level employs the AES-256 algorithm, complying with the highest standards and ensuring the security of data transmission and storage. The intrusion detection rate reached 97%, surpassing the 95% requirement, indicating the system’s ability to effectively identify and respond to potential cyberattacks.

The access control policy also performed excellently, with 100% compliance in all user permissions and access behaviors. The system achieved vulnerability remediation within 18 hours after detection, significantly faster than the 24-hour target, enhancing its responsiveness to emerging security threats. Additionally, testing of the system’s data backup and recovery strategy showed that both backup and recovery processes were completed successfully at a 100% success rate, further improving data security and business continuity. The intelligent power monitoring system performs exceptionally well in terms of security, demonstrating robust and effective protection capabilities.