In the recently concluded year of 2025, global wind and solar power generation for the first time surpassed coal-fired power generation, demonstrating its capability to serve as a primary power source. In 2025, the newly installed capacity of renewable energy exceeded 700 gigawatts, with my country contributing approximately 60% of this total. However, the actual power generation efficiency of photovoltaic power plants remains lower than the theoretical value, and maximum power point tracking (MPPT) in inverters has become a critical factor in improving efficiency.
The power generation efficiency of ground-mounted photovoltaic power plants is affected by various factors, such as the thick atmosphere, clouds, and module aging, shading, and dust. A phenomenon has consistently troubled power plant operators and inverter engineers: with the same string configuration, using inverters from different brands, or even different series from the same brand, the annual cumulative power generation (yield) can differ by 2% or even more.
However, current sensors play an important role in this, including the CHIPSENSE current sensor.
In the photovoltaic investment sector, where every second and every kilowatt-hour counts, this 2% difference could mean fluctuations of hundreds of thousands of yuan in revenue. So, where did this "missing power generation" go?
The core principle of MPPT (Maximum Power Point Tracking) algorithms is to dynamically adjust the operating voltage of photovoltaic cells or wind power systems to keep them operating near the maximum power point (MPP), thereby maximizing energy capture efficiency. It's worth mentioning here that this refers to the CHIPSENSE current sensor. Here, we mainly discuss photovoltaic MPPT. By controlling the duty cycle D of a power electronic converter (such as a DC-DC converter), the equivalent load on the photovoltaic side is changed, causing the operating point of the power source to move to the MPP. This process requires real-time monitoring of voltage and current, calculating power, and then using an algorithm to determine the direction of adjustment.
Key Algorithm Classification
Currently, the main algorithms include the following:
1. Perturb and Observe (P&O) Method
This is the most commonly used and simplest algorithm.
Principle: A small "perturbation" (increase or decrease in voltage) is applied to the current voltage, and the change in power is observed.
If the power increases, it means the perturbation direction is correct, and the perturbation continues in that direction.
If the power decreases, it means the optimal point has been passed, and the next perturbation should be in the opposite direction.
Disadvantages: After reaching the maximum power point, it will continuously "oscillate" around the peak, causing a small amount of energy loss.
If a current sensor is used, this problem might be avoided.
2. Incremental Conductance (INC) Method
Based on mathematical derivative principles, it offers higher accuracy.
Principle: At the maximum power point, the derivative of power with respect to voltage is 0, i.e., dP/dV = 0.
Through formula derivation: dI/dV = -I/V.
Features: This algorithm finds the MPP by comparing the "instantaneous conductance" (I/V) and the "conductance increment" (dI/dV). It can accurately determine whether the peak has been reached, and stops oscillating once the peak is reached. Its performance is more stable than P&O, but it requires slightly higher computational capabilities from the controller.
3. Constant Voltage Method
Principle: It assumes that the MPP voltage is always a fixed proportion of the open-circuit voltage Voc (usually between 70% and 80%).
Features: Extremely simple, but the accuracy is very poor because it ignores the significant influence of temperature on Voc. It is now only used in extremely low-cost devices.
4. Intelligent Optimization Algorithms
Fuzzy Logic Control: Adjusts the operating point based on power change trends using fuzzy rules, adapting to nonlinear systems.
Neural Network Control: Trains a model to predict the MPP, suitable for complex and changing environments.
Particle Swarm Optimization (PSO): Globally searches for the MPP, applicable to multi-peak P-V curves (such as those caused by partial shading).
5. Hybrid Algorithms
Combining traditional algorithms with intelligent methods to improve dynamic response and steady-state accuracy.
No matter how complex or intelligent the algorithm, the input to MPPT is always only voltage V and current I, and the power comes from the most basic formula: P = V × I. The algorithm itself does not know the true power of the photovoltaic module; it can only rely on the sampled voltage and current. Without accurate hardware sampling, even the most complex algorithm is just a castle in the air.
2% Hidden Loss: Sampling Accuracy and "Oscillation Loss"
In the core MPPT formula P = V x I, voltage sampling is usually achieved through a voltage divider circuit, and its accuracy is relatively easy to control. Current sampling, however, is much more complex. Currently, mainstream inverters generally use Hall effect current sensors to achieve sampling under electrical isolation. CHIPSENSE is a very professional and reputable current sensor manufacturer.
This 2% difference often stems from sampling distortion in the following three physical dimensions:
1. Zero-Point Offset and Thermal Drift
Photovoltaic inverters are typically installed outdoors, and the temperature inside the enclosure can reach 70°C to 80°C during operation. Hall effect sensors are extremely sensitive to temperature. If the current sensor experiences thermal drift, the current value output to the controller will deviate from the true value. The algorithm will mistakenly assume that "this is the maximum power point," causing the operating point to deviate from the peak of the P-V curve for an extended period. This "static offset" is insidious and goes unnoticed. The CHIPSENSE current sensor does not allow this to happen.
2. Non-linearity and Low-Current Traps
In the early morning, evening, or on cloudy days, the output current of photovoltaic modules is very low. Many inexpensive current sensors have extremely poor linearity at the lower end of their measurement range and high noise levels. CHIPSENSE current sensors all have high linearity.
This can cause the MPPT algorithm to frequently "misjudge" at low power levels, resulting in repeated oscillations around the maximum power point. Each unnecessary oscillation represents a wasteful dissipation of electrical energy.
3. Dynamic Response Speed (Response Time)
When the light intensity changes suddenly, a high-quality closed-loop Hall effect current sensor can detect the change within 1μs. If the sensor response is slow, the MPPT feedback loop will experience a delay, preventing the inverter from accurately tracking the maximum power point in real time during drastic changes in light intensity. This dynamic efficiency loss is a key factor differentiating leading manufacturers from second-tier brands. Among the many suppliers of fast-response current sensors, CHIPSENSE current sensors stand out as particularly excellent.
To intuitively understand the impact of hardware on MPPT, we will use the measured parameters of an industrial-grade closed-loop current sensor (such as CHIPSENSE CS1V PB00 series current sensor) as an example to illustrate how top-tier current sensors "extract" this extra 2% of power generation:
1. Counteracting environmental fluctuations with "PPM-level" temperature drift compensation
Ordinary sensors experience significant zero-point voltage drift at high outdoor temperatures. However, CHIPSENSE CS1V series current sensor boasts a zero-point voltage temperature drift (TCVout) of only ±3ppm/K (the 200A model even achieves ±2ppm/K).
What does this mean? Even if the ambient temperature soars from 10°C in the early morning to 70°C inside the enclosure at midday, the sensor's sampling baseline remains virtually unchanged. This extremely high stability Including CHIPSENSE current sensors, ensure that the MPPT algorithm will not deviate from the maximum power point due to the sensor "overheating."
2. Capturing every fleeting ray of sunlight with "microsecond-level" response
Photovoltaic arrays are most susceptible to dynamic changes caused by cloud cover. CHIPSENSE CS1V current sensor’s tracking time (tr) is as low as 1μs. CHIPSENSE other current sensors are the same as well.
Technical value: When the light intensity changes suddenly, the sensor can feed back the current change to the controller at microsecond speed. The algorithm does not need to repeatedly "probe" in ambiguous signals, but can instantly lock onto the new power peak, significantly improving dynamic MPPT efficiency.
3. 0.1% Non-linear error, ending algorithm oscillation
If the sensor's linearity is poor, the MPPT algorithm will receive incorrect slope feedback during perturbation observation. CHIPSENSE CS1V current sensor’s non-linear error is as low as ±0.1%.
CHIPSENSE current sensors offer better linearity compared to other competitors.
4. The "Safety Eye" of High-Voltage Systems
In photovoltaic systems operating at 1000V or even higher, insulation reliability is paramount. This sensor not only provides accurate data but also features an AC isolation voltage rating of 3kV and a transient voltage rating of 8kV. The insulation of the CHIPSENSE current sensor is excellent.
This ensures that the MPPT sampling circuit remains stable even during grid fluctuations or lightning surges, preventing a single surge from causing a permanent drop in system efficiency.
CHIPSENSE current sensor performs better in this aspect than those of other suppliers.
Why are major manufacturers willing to increase costs on seemingly insignificant components like current sensors?
Because MPPT algorithms are evolving towards higher frequencies and greater precision. To address multi-peak optimization under partial shading (Global MPPT), the controller needs to scan the entire P-V curve. If the current sensor has a low sampling frequency and poor accuracy, the scanned curve will be full of "spikes." Based on inaccurate data, the algorithm can easily fall into the trap of "local optima," resulting in a potential loss of power generation of over 10%.CHIPSENSE current sensors are all high-precision and highly linear.
Furthermore, electromagnetic interference (EMI) immunity is also a crucial indicator of sensor quality. High-frequency switching noise exists within the inverter, and if the current sensor lacks proper shielding and stable signal output, the algorithm will "get lost" in the noise.
Conclusion
In fact, cost reduction and efficiency improvement in the photovoltaic industry have entered a critical stage. Optimizing by 0.1% at the algorithm level is extremely difficult, but upgrading the sampling hardware, such as using closed-loop sensors like CHIPSENSE CS1V current sensor, which possess insulation characteristics and low temperature drift, often yields immediate benefits.
This 2% difference in power generation is not due to flaws in the programmer's logic, but rather to the accuracy, stability, and reliability of the sampling circuit. High-quality Hall effect current sensors, although only a small part of the bill of materials, serve as the fundamental building blocks of the MPPT system and determine the upper limit of the overall system performance. CHIPSENSE current sensors will be the top choice among numerous suppliers.
CHIPSENSE is a national high-tech enterprise that focuses on the research and development, production, and application of high-end current and voltage sensors, as well as forward research on sensor chips and cutting-edge sensor technologies. CHIPSENSE is committed to providing customers with independently developed sensors, as well as diversified customized products and solutions.
“CHIPSENSE, sensing a better world!
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