Sistem Din Aikatau Mai Yawan Fushen Karamin AI Don Iyakokin Ingantaccen & Tattalin Arzikin Aiki

A halin da ake kula da tushen tushen zafi a cikin gwamnati na farko da kuma aiki na musamman ta hanyar zafi suna da abubuwan da suka faruwa - masu yawan zafi mai girma. Saboda haka, muhimmin matalauta, musamman yadda ake yi aiki da frequency, suna da muhimmanci a gaba. Tushen tushen zafi na business da take da IEE-Business suna iya kawo wahala wannan a haɗa da amfani da AI don samun inganci da zama a kan aiki da frequency regulation. Su ne ake iya bincike frequency a baya, amsa da jumla a fili da tsawon seconds, aiki da rarrabe da aiki mai yawa, da kuma a yi aiki da wurare da dama. Wannan yana taimakawa wajen sauyin zama a kan grid da kuma a tabbatar da aiki da tushen tushen zafi.

1 Bincike Tsaro
1.1 Tsaro Mai Aiki

Idan an yi aiki da tushen tushen zafi na grid-frequency regulation, a maɓallin da yaɗa ita ce a nufin abubuwan da za su iya aiki don a bayyana frequency changes da kuma saukar zama. Abubuwan da za su iya aiki sun hada da:

• Bincike Frequency a Baya: A yi amfani da sensors mai kyau don ina ƙara frequency shifts kai tsaye, sannan a bayyana data zuwa CPU na baya.

• Amsa da Jumla a Filii: A yi amsa da jumla a fili da tsawon milliseconds don in iya amsa da jumla frequency changes da kuma in a kawo wahala da deviation.

• Algorithms na Rarrabe Da Aiki Mai Yawa: A yi amfani da models mai yawa (fuzzy logic, genetic algorithms, deep learning) don in a yi rarrabe da aiki mai yawa da kuma in a kawo wahala da energy efficiency.

• Interface na Grid Operator Communication: A bayyana interfaces na standard don in a yi integration da grid dispatch centers don in a bayyana commands da kuma in a tattauna system status.

1.2 Tsaro Na Aiki

Don in tabbatar da aiki da inganci da tushen tushen zafi na grid-frequency regulation, yawancin abubuwan da za su iya aiki sun hada da:

• Lokacin Amsa: Lokaci daga lokacin da system ya samu signal da frequency deviation zuwa lokacin da ya faru a yi amsa da jumla charging/discharging state ba zai iya fi 100 milliseconds, wanda yake taimakawa a yi amsa da jumla a baya da frequency changes na grid.

• Zama Na Amsa Da Jumla: Ba a bayyana frequency ±0.01Hz daga target frequency ba, wanda yake taimakawa a saukar zama a kan power system da kuma quality of power supply.

• Zama Na System: System ya kamata a zama da fault tolerance mai yawa. Ya kamata a yi aiki da normal a cikin lokutan da ke da yawa ko a lokutan da ke da shiga, tare da annual average downtime ba zai iya fi 2 hours.

• Gaskiya: System ya kamata a yi amsa da jumla strategy na frequency regulation a kan wurare da dama (e.g., peak periods, off-peak periods). Wannan yana taimakawa a yi aiki da effective participation a kan grid frequency regulation a kan wurare, wanda yake taimakawa a saukar flexibility da resilience na grid. Kuma, system ya kamata a yi amfani da scalability da upgradeability mai yawa don in a yi fitarwa a kan future power market da technological development needs.

2 Design Na AI Don Grid Frequency Regulation System
2.1 Real-Time Monitoring & Prediction Module

Wannan module, wanda yake a matsayin cornerstone na intelligent C&I energy storage systems, ana amfani da advanced ML algorithms don in bincike frequencies na grid a baya da kuma in bincike trends. Yana taimakawa a yi decision-making proactive don frequency regulation through:

• Sensors na high-precision a kan grid nodes take ƙara data na frequency a baya, sannan a bayyana zuwa CPU.

• Time-series models (ARIMA/LSTM) take a yi training a kan historical data don in a bayyana patterns da periodicities.

• Predictive analytics take a yi forecasting da frequency trends (seconds to minutes ahead) based on current/historical states, guiding storage system strategies.

2.2 Rapid-Response Charge-Discharge Control Module

Wannan module take a yi adjustment da charge-discharge states na energy storage system a baya based on grid frequency changes da kuma predictions, using intelligent algorithms (PID/fuzzy logic) don in a yi dynamic control da power da kuma in saukar frequency na grid.

• Low-frequency response: Take a yi trigger da energy injection via storage unit discharge.

• High-frequency response: Take a yi absorption da excess energy through charging.

• Millisecond-level speed: Take a yi amfani da RTOS don instant command delivery, with closed-loop feedback to monitor and adjust strategies until frequency normalizes.

2.3 Intelligent Scheduling & Optimization Module

Wannan module, wanda yake a matsayin critical part na intelligent commercial energy storage systems, take a yi amfani da AI don in yi optimization da scheduling strategies—balancing frequency regulation effectiveness da economic costs. By applying machine learning (genetic algorithms, particle swarm optimization, deep learning), it predicts grid load demands da kuma renewable energy output don in a yi creation da optimal charge-discharge plans. Below is a simplified code example using genetic algorithms for optimization:

2.4 System Self-adaptation and Learning Module

System self-adaptation and learning module, wanda yake a matsayin key component na intelligent commercial and industrial energy storage system, take a yi amfani da methods like reinforcement learning da kuma deep learning, take a yi self-adjustment based on historical da real-time data. Wannan take a yi adaptation a kan dynamic changes na grid loads da kuma uncertainties na renewable energy. For instance, reinforcement learning can learn optimal strategies through interactions with the environment. Below is a conceptual code snippet demonstrating how to use reinforcement learning to optimize frequency regulation decisions:

3 Hardware Design
3.1 Server Configuration

Core computing na grid frequency regulation system na intelligent commercial and industrial energy storage take a yi amfani da high-performance servers. Wadannan take a taimakawa efficient real-time data analysis, AI algorithm operation, da kuma rapid processing da large-scale data. Given the need to handle massive real-time and historical data, and perform complex calculations and model training, server configurations are as follows:

• Processor: Intel Xeon Platinum 8380 or equivalent CPU (high core count, high frequency for strong parallel processing).

• Memory: 128GB–256GB DDR4 ECC (high-speed access, error checking for data integrity).

• Storage: NVMe SSD (system disk, fast read/write for OS and app responsiveness) + large-capacity SAS HDD (data disk for historical data storage).

• GPU Acceleration: NVIDIA Tesla T4 GPU (for compute-intensive tasks like deep learning, accelerating model training/prediction).

• Network Interface: 10GbE network card (high-speed data transfer for real-time communication).

3.2 Storage Device Configuration

To support real-time decision-making and historical data analysis, storage devices need high read/write speeds and large capacities:

• System Disk: 1TB NVMe SSD (low latency, high IOPS for fast OS/app startup).

• Data Storage Disk: 10TB SAS HDD (stores historical frequency data, electricity price info, system logs for analysis/auditing).

• Backup & Disaster Recovery: RAID 5/6 arrays (data redundancy to prevent single-point failure data loss); regular off-site backups to remote data centers (ensures data security).

3.3 Network Device Configuration

Network device selection directly impacts real-time data transmission and security. For the grid frequency regulation system of intelligent commercial energy storage, recommendations include:

• Core Switch: Cisco Catalyst 9500 series (or equivalent) with 100GbE ports for high-speed, high-bandwidth data exchange.

• Firewall: Next-gen solutions (e.g., Fortinet FortiGate) for intrusion detection, virus protection, and application control to secure the network.

• VPN: Encrypted VPN tunnels for secure remote O&M and communication with grid operators, protecting sensitive data from interception/tampering.

3.4 I/O Device Configuration

To enable data collection and human-machine interaction, high-performance I/O devices ensure accurate data capture and intuitive display:

• Sensors: High-precision current/voltage transformers at key grid nodes, monitoring frequency/voltage/current with ≥1kHz sampling rates.

• Display Terminal: Large-size, high-resolution industrial touchscreens for system status monitoring and manual operations.

• Communication Interfaces: Standard interfaces (RS-485, Ethernet, fiber) for stable connectivity with external devices/systems.

• Alarm System: Integrated audio-visual alarms triggering on anomalies (e.g., frequency violations, equipment faults) to prompt operator intervention.

5 Conclusion

This paper introduces the design of a grid frequency regulation system for intelligent commercial and industrial energy storage systems, covering demand analysis, functional design, hardware design, and operation testing. Leveraging artificial intelligence technologies, the system enables real-time grid frequency monitoring and rapid response, enhancing the stability and reliability of the power grid.