The proliferation of photovoltaic (PV) power generation as a cornerstone of sustainable energy systems brings with it the persistent challenge of efficiency degradation under non-ideal environmental conditions. A significant performance bottleneck occurs during periods of low irradiance, such as overcast or rainy days, as well as during early mornings and late afternoons. Under these conditions, the output power of PV arrays can drop precipitously, often falling well below the rated input power of the connected solar inverters. Since the conversion efficiency of solar inverters is highly dependent on the input power level—typically exhibiting a sharp decline when the input is less than 20% of the inverter’s rated capacity—the overall system efficiency suffers dramatically. Conventional system design, which pairs a fixed PV array configuration with a dedicated solar inverter based on standard test condition ratings, fails to address this mismatch. This paper presents a comprehensive group control strategy and its implementation method for a network of distributed PV arrays and multiple solar inverters, aimed at dynamically optimizing power matching to enhance energy yield during suboptimal weather.
The typical architecture of a distributed PV system, as shown in the referenced work, involves ‘n’ independent PV array groups each feeding into their own dedicated grid-tied solar inverter, with the AC outputs combined before connecting to the utility. This static topology lacks the flexibility to reconfigure the electrical relationships between energy sources (arrays) and conversion units (solar inverters) when conditions change. To overcome this limitation, we propose a dynamic, switch-based topology network. This system employs a switching matrix to interconnect ‘m’ PV arrays with ‘n’ solar inverters, forming a reconfigurable network. The core idea is to treat the arrays and inverters as a pool of resources that can be dynamically grouped and connected in series-parallel combinations to best match the available PV power to the most efficient operating points of the available solar inverters.
The topology is fundamentally structured around two primary switching matrices: one for series connections (S) and one for parallel connections (P). The series switch matrix, denoted as an m×m matrix S, governs how PV arrays are connected in series to form strings of higher voltage. Its elements, 0 or 1, represent the open or closed state of the connection switches between array terminals. A practical implementation involves arranging arrays in a sequence of alternating polarities (“positive-negative-positive-negative…”) with a decreasing number of connection switches for subsequent arrays. This design allows the formation of any series string from the available array pool. The parallel switch matrix, denoted as an m×n matrix P, determines how the resulting series strings (or individual arrays) are connected to the inputs of the various solar inverters. Each column in P corresponds to an inverter, and each row to a PV array/string; a ‘1’ indicates a connection.
The mathematical formulation of the system’s total AC output power, P_out, is expressed as a function of the array outputs, the switch matrices, and the efficiency characteristics of the solar inverters:
$$P_{out} = \sum_{i=1}^{n} (P_{in,i} \cdot \epsilon_i) = \sum_{i=1}^{n} \left( \mathbf{PV}_{1 \times m} \cdot \mathbf{S}_{m \times m} \cdot \mathbf{P}_{m \times n} \right)_i \cdot \epsilon_i$$
where \(\mathbf{PV}\) is a row vector of the instantaneous output powers from the m PV arrays, \(\mathbf{S}\) is the series connection matrix, \(\mathbf{P}\) is the parallel connection matrix, and \(\epsilon_i\) is the conversion efficiency of the i-th solar inverter, which itself is a function of its input power \(P_{in,i}\). For any solar inverter \(i\) with a rated power \(P_{r,i}\), maximum input current \(I_{r,i}^{max}\), and input voltage range \([V_{r,i}^{min}, V_{r,i}^{max}]\), the electrical constraints for a valid configuration are:
Voltage Constraint:
$$V_{r,i}^{min} < V_{in,i} = \sum_{j \in \text{String}_i} V_{array,j} \leq V_{r,i}^{max}$$
Current Constraint:
$$0 < I_{in,i} = \sum_{j \in \text{String}_i} I_{array,j} \leq I_{r,i}^{max}$$
Here, \(I_{array,j}\) for a series string is determined by the array with the minimum current output in that string, a critical consideration for partial shading or mismatch.
Adaptive Clustering Algorithm for Group Control
The central intelligence of the proposed system is an adaptive clustering algorithm. This algorithm dynamically classifies PV arrays with similar operational characteristics (primarily current output) and groups them into clusters. These clusters are then mapped onto the available solar inverters in a way that maximizes the total system output power while adhering to the inverter constraints. The algorithm treats each PV array (or formed series string) and each solar inverter as an object in a clustering process, with the “distance” measure being the mismatch in operational parameters like voltage.
The algorithm proceeds in a hierarchical, agglomerative manner. It starts with each PV array as a separate cluster. At each step, it identifies the two clusters (which could be arrays, strings, or an existing cluster and an inverter) with the smallest “feature distance,” typically the difference in their output voltage for parallel connection considerations. These two clusters are merged. The process repeats, updating the similarity matrix at each step, until a termination condition is met. The algorithm’s execution is logically divided into control actions for the parallel switch matrix P and the series switch matrix S.
Parallel Switch Matrix (P) Control Flow
The control logic for the P matrix focuses on matching the voltage of PV strings to the optimal input voltage ranges of the solar inverters. After extracting the voltage and current features of each series string, the algorithm calculates the Euclidean distance between the string’s voltage and the preferred operating voltage of each solar inverter:
$$d(V_{string}, V_{inv}) = \sqrt{ \sum_{k} (V_{string,k} – V_{inv,k})^2 }$$
The algorithm then connects the string to the inverter with the smallest feature distance, provided the current summation constraint is not violated. After connection, the combined cluster’s features are re-evaluated. The merger is accepted only if it increases the overall similarity (reduces mismatch), thereby moving the inverter’s input power closer to its rated optimal point and improving its conversion efficiency \(\epsilon\). This process iterates until all PV strings are assigned to an inverter.
Series Switch Matrix (S) Control Flow
The control logic for the S matrix focuses on forming series strings from individual arrays. The primary objective is to minimize the current mismatch within a string, as the string current is limited by the array with the lowest output. The algorithm uses a model to predict array current based on measured parameters like short-circuit current (\(I_{sc}\)), open-circuit voltage (\(V_{oc}\)), and maximum power point parameters (\(I_m, V_m\)):
$$I = I_{sc} \left[ 1 – C_1 \left( e^{V/(C_2 V_{oc})} – 1 \right) \right]$$
$$\text{where } C_1 = (1 – I_m/I_{sc}) e^{-V_m/(C_2 V_{oc})} \text{ and } C_2 = (V_m/V_{oc} – 1)\left[ \ln(1 – I_m/I_{sc}) \right]^{-1}$$
The algorithm clusters arrays with the most similar predicted or measured currents for series connection. The output current of the resulting series string \(I_{string}\) is:
$$I_{string} = \min(I_{array,1}, I_{array,2}, …, I_{array,k})$$
This string is then passed to the parallel control logic for assignment to a solar inverter.
Integrated Algorithm and Trigger Condition
The overall adaptive clustering algorithm integrates both S and P control flows. It operates in cycles: 1) Measure system output power \(P_{out}^{current}\). 2) Based on array current features, form or modify series strings using S-control logic. 3) Assign strings to solar inverters using P-control logic. 4) Measure new system power \(P_{out}^{new}\). 5) If \(P_{out}^{new} > P_{out}^{current}\), accept the new configuration; otherwise, revert and try an alternative clustering. To prevent excessive switching and instability, the algorithm is not run continuously. An adaptive trigger condition based on irradiance deviation is used. A predicted irradiance profile \(lg_k(s)\) is constructed from the average of the previous days’ profiles:
$$lg_k(s) = \text{AVERAGE} \{ r_{k-i}(s), i=1,2,3,4,5 \}$$
The algorithm is triggered only if the actual irradiance \(r_k(s)\) deviates from this prediction by more than a threshold \(r_0\) for a sustained period (e.g., 10 minutes):
$$| r_k(s) – lg_k(s) | \geq r_0$$
This ensures the system reconfigures primarily in response to significant weather changes, such as the onset of cloudy conditions, rather than minor fluctuations.
Simulation Analysis and Case Study
A simulation model of a ~600 kW system was developed to validate the group control strategy. The system comprised 20 PV arrays (modeled with parameters from Canadian Solar CS5P-220M modules under varying irradiance) and 6 solar inverters of different power ratings. To simulate a cloudy day, the irradiance and temperature for each array were set to low but slightly varying values, introducing realistic mismatch.
Table 1: PV Array Meteorological Parameters (Simulation)
| Array No. | Temperature (°C) | Irradiance (W/m²) |
|---|---|---|
| 1-5 | 15.0 – 15.4 | 100 – 104 |
| 6-10 | 15.5 – 15.9 | 105 – 109 |
| 11-15 | 16.0 – 16.4 | 110 – 114 |
| 16-20 | 16.5 – 16.9 | 115 – 119 |
Table 2: Solar Inverter Electrical Parameters
| Inverter No. | Rated Power (kW) | Max Input Voltage (V) | Min Input Voltage (V) | Max Input Current (A) |
|---|---|---|---|---|
| INV-1 | 200 | 880 | 350 | 300 |
| INV-2 | 150 | 880 | 350 | 250 |
| INV-3 | 100 | 880 | 350 | 200 |
| INV-4 | 70 | 880 | 350 | 130 |
| INV-5 | 50 | 450 | 220 | 150 |
| INV-6 | 30 | 450 | 220 | 130 |
Scenario 1: Conventional Static Topology. The 20 arrays were statically divided into 6 groups connected to the 6 solar inverters. Under the low-irradiance conditions, the input power to each solar inverter was far below its rating: P_in = [37, 28, 24, 22, 18, 16] kW. The total DC input was 145 kW. Due to the low efficiency of the severely underloaded solar inverters, the total AC output was only 98 kW, resulting in a system conversion efficiency of approximately 67.6%.
Scenario 2: Dynamic Group Control Topology. The adaptive clustering algorithm was enabled. It dynamically reconfigured the arrays into three optimal clusters. It powered down the three largest solar inverters (INV-1, INV-2, INV-5) and concentrated the available DC power onto INV-3, INV-4, and INV-6 with input powers of 68 kW, 55 kW, and 22 kW respectively (total DC input remains 145 kW). By operating these three solar inverters much closer to their rated capacity, their conversion efficiency increased significantly. The total AC output power rose to 130 kW, yielding a system efficiency of 89.7%.
Table 3: Performance Comparison Before and After Group Control
| Solar Inverter | Static Topology Input (kW) | Static Output (kW) | Group Control Input (kW) | Group Control Output (kW) | State after Control | |
|---|---|---|---|---|---|---|
| INV-1 (200kW) | 37 | 98 | 0 | 130 | OFF | |
| INV-2 (150kW) | 28 | 0 | OFF | |||
| INV-3 (100kW) | 24 | 68 | ON | |||
| INV-4 (70kW) | 22 | 55 | ON | |||
| INV-5 (50kW) | 18 | 0 | OFF | |||
| INV-6 (30kW) | 16 | 22 | ON | |||
| System Efficiency | 67.6% | System Efficiency | 89.7% | |||
The simulation demonstrated a 32.7% increase in AC output power ((130-98)/98) under low-irradiance conditions, validating the efficacy of the group control strategy in improving the utilization of solar inverters.
Experimental Verification and Analysis
A scaled-down experimental platform was constructed to further verify the concept. It consisted of six 100W solar panels, two solar inverters (200W and 400W), a relay-based switching matrix, an MPPT tracking circuit, a DSP28335 controller, and a monitoring PC. Data on power output and irradiance were collected on both sunny and cloudy days.
On a sunny day, with irradiance high and the solar inverters already operating near their optimal points, activating the group control strategy yielded a modest 4.1% power improvement. The major benefit was observed under cloudy conditions. Initially, the system was configured statically: two panels to the 200W solar inverter and four panels to the 400W solar inverter. Both solar inverters were severely underloaded, leading to low efficiency. After 50 seconds, the group control algorithm was triggered. It reconfigured the network, shutting down the 400W solar inverter and connecting all six panels to the 200W solar inverter. This consolidation dramatically increased the input power to the single operating solar inverter, pushing it into a higher-efficiency region of its curve. The monitored AC output power increased by approximately 34.0% after reconfiguration. Measurements of the output current from the solar inverters clearly showed the 200W inverter’s current rising post-control, while the 400W inverter’s current dropped to zero after being deactivated. This experiment conclusively proved that the dynamic group control strategy is most effective under the low-irradiance conditions it was designed for, with minimal impact on already-optimal sunny-day operation.
Conclusion and Future Perspectives
This paper has presented a viable and effective solution to the problem of poor solar inverter efficiency during periods of low PV array output, such as on cloudy and rainy days. By adopting a reconfigurable switch matrix topology and an adaptive clustering algorithm for group control, the system dynamically optimizes the mapping between distributed PV arrays and multiple solar inverters. The strategy ensures that the available DC power is concentrated onto a subset of solar inverters, driving their input power closer to the rated optimum and thereby maximizing the overall AC power generation efficiency of the network. Both simulation and experimental results confirm significant performance gains in unfavorable weather conditions.
While the initial implementation involves a switching matrix that adds to the system’s capital cost and complexity, the substantial increase in energy yield during commonly occurring suboptimal conditions offers a compelling return on investment over the system’s lifetime by enhancing weather resilience. Future work will focus on the integration and miniaturization of the control hardware. The discrete relay-based matrix can be replaced with integrated high-current solid-state switching circuits, and the control logic can be embedded into a more compact, dedicated “PV Group Controller” unit. This will improve the reliability, reduce the footprint, and lower the cost of the system, facilitating its practical adoption in commercial and utility-scale photovoltaic power plants. The core principle of intelligent, dynamic resource pooling for optimal solar inverter operation remains a powerful strategy for maximizing the return from photovoltaic assets.